Merge remote-tracking branch 'origin/master'

.gitignore

@@ -10,13 +10,9 @@ poetry.toml
 **/log/
 *.spec
 dist/
-*steps.zip
-*.pkl
-best_model.zip
+*.zip
 *.csv
-*.npz
-*.xml
 *.json
-*.yaml
-*.iml
-*.TXT
+*.xml
+*.npz
+*.pkl

behaviors/custom/reinforcement/walk/walk.py

@@ -98,12 +98,8 @@ class Walk(Behavior):
         velocity[0] = np.clip(velocity[0], -0.5, 0.5)
         velocity[1] = np.clip(velocity[1], -0.25, 0.25)
 
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
+        radian_joint_positions = np.deg2rad(list(robot.motor_positions.values()))
+        radian_joint_speeds = np.deg2rad(list(robot.motor_speeds.values()))
 
         qpos_qvel_previous_action = np.vstack(
             (

command.md

@@ -1,14 +0,0 @@
-训练（默认）
-bash train.sh
-
-测试（实时+显示画面)
-GYM_CPU_MODE=test GYM_CPU_TEST_MODEL=scripts/gyms/logs/Walk_R0_005/best_model.zip GYM_CPU_TEST_FOLDER=scripts/gyms/logs/Walk_R0_005/ GYM_CPU_TEST_NO_RENDER=0 GYM_CPU_TEST_NO_REALTIME=0 bash train.sh
-
-测试（无画面、非实时）
-GYM_CPU_MODE=test GYM_CPU_TEST_NO_RENDER=1 GYM_CPU_TEST_NO_REALTIME=1 bash train.sh
-
-retrain（继续训练）
-GYM_CPU_MODE=train GYM_CPU_TRAIN_MODEL=scripts/gyms/logs/Walk_R0_005/best_model.zip bash train.sh
-
-retrain+改训练超参
-GYM_CPU_MODE=train GYM_CPU_TRAIN_MODEL=scripts/gyms/logs/Walk_R0_004/best_model.zip GYM_CPU_TRAIN_LR=2e-4 GYM_CPU_TRAIN_CLIP_RANGE=0.13 GYM_CPU_TRAIN_BATCH_SIZE=256 YM_CPU_TRAIN_GAMMA=0.95 GYM_CPU_TRAIN_ENT_COEF=0.05 GYM_CPU_TRAIN_EPOCHS=8 bash train.sh

communication/server.py

@@ -1,4 +1,5 @@
 import logging
+import os
 import socket
 import time
 from select import select
@@ -15,6 +16,11 @@ class Server:
         self.__socket: socket.socket = self._create_socket()
         self.__send_buff = []
         self.__rcv_buffer_size = 1024
+        self.__rcv_buffer_default_size = 1024
+        self.__max_msg_size = 1048576
+        self.__shrink_threshold = 8192
+        self.__shrink_after_msgs = 200
+        self.__small_msg_streak = 0
         self.__rcv_buffer = bytearray(self.__rcv_buffer_size)
 
     def _create_socket(self) -> socket.socket:
@@ -105,6 +111,10 @@ class Server:
 
             msg_size = int.from_bytes(self.__rcv_buffer[:4], byteorder="big", signed=False)
 
+            # Guard against corrupted frame lengths that would trigger huge allocations.
+            if msg_size <= 0 or msg_size > self.__max_msg_size:
+                raise ConnectionResetError
+
             if msg_size > self.__rcv_buffer_size:
                 self.__rcv_buffer_size = msg_size
                 self.__rcv_buffer = bytearray(self.__rcv_buffer_size)
@@ -120,6 +130,15 @@ class Server:
                 message=self.__rcv_buffer[:msg_size].decode()
             )
 
+            if msg_size <= self.__shrink_threshold and self.__rcv_buffer_size > self.__rcv_buffer_default_size:
+                self.__small_msg_streak += 1
+                if self.__small_msg_streak >= self.__shrink_after_msgs:
+                    self.__rcv_buffer_size = self.__rcv_buffer_default_size
+                    self.__rcv_buffer = bytearray(self.__rcv_buffer_size)
+                    self.__small_msg_streak = 0
+            else:
+                self.__small_msg_streak = 0
+
             # 如果socket没有更多数据就退出
             if len(select([self.__socket], [], [], 0.0)[0]) == 0:
                 break

readme.md

@@ -74,6 +74,21 @@ poetry run ./build_binary.sh <team-name>
 
 Once binary generation is finished, the result will be inside the build folder, as ```<team-name>.tar.gz```
 
+### GYM
+
+To use the gym, you need to install the following dependencies:
+```bash
+pip install gymnasium
+pip install psutil
+pip install stable-baselines3
+```
+
+Then, you can run gym examples under the ```GYM_CPU``` folder:
+```bash
+python3 -m scripts.gyms.Walk # Run the Walk gym example
+                             # of course, you can run other gym examples
+```
+
 ### Authors and acknowledgment
 This project was developed and contributed by:
 - **Chenxi Liu**

scripts/commons/Server.py

@@ -1,9 +1,14 @@
 import subprocess
 import os
 import time
+import threading
 
 
 class Server():
+    WATCHDOG_ENABLED = True
+    WATCHDOG_INTERVAL_SEC = 30.0
+    WATCHDOG_RSS_MB_LIMIT = 2000.0
+
     def __init__(self, first_server_p, first_monitor_p, n_servers, no_render=True, no_realtime=True) -> None:
         try:
             import psutil
@@ -14,6 +19,10 @@ class Server():
         self.first_server_p = first_server_p
         self.n_servers = n_servers
         self.rcss_processes = []
+        self._server_specs = []
+        self._watchdog_stop = threading.Event()
+        self._watchdog_lock = threading.Lock()
+        self._watchdog_thread = None
         first_monitor_p = first_monitor_p + 100
 
         # makes it easier to kill test servers without affecting train servers
@@ -23,26 +32,79 @@ class Server():
         for i in range(n_servers):
             port = first_server_p + i
             mport = first_monitor_p + i
+            self._server_specs.append((port, mport, cmd, render_arg, realtime_arg))
+            proc = self._spawn_server(port, mport, cmd, render_arg, realtime_arg)
+            self.rcss_processes.append(proc)
 
-            server_cmd = f"{cmd} -c {port} -m {mport} {render_arg} {realtime_arg}".strip()
+        if self.WATCHDOG_ENABLED:
+            self._watchdog_thread = threading.Thread(target=self._watchdog_loop, daemon=True)
+            self._watchdog_thread.start()
 
-            proc = subprocess.Popen(
-                server_cmd.split(),
-                stdout=subprocess.DEVNULL,
-                stderr=subprocess.STDOUT,
-                start_new_session=True
+    def _spawn_server(self, port, mport, cmd, render_arg, realtime_arg):
+        server_cmd = f"{cmd} -c {port} -m {mport} {render_arg} {realtime_arg}".strip()
+
+        proc = subprocess.Popen(
+            server_cmd.split(),
+            stdout=subprocess.DEVNULL,
+            stderr=subprocess.STDOUT,
+            start_new_session=True
+        )
+
+        # Avoid startup storm when launching many servers at once.
+        time.sleep(0.03)
+
+        rc = proc.poll()
+        if rc is not None:
+            raise RuntimeError(
+                f"rcssservermj exited early (code={rc}) on server port {port}, monitor port {mport}"
             )
 
-            # Avoid startup storm when launching many servers at once.
-            time.sleep(0.03)
+        return proc
 
-            rc = proc.poll()
-            if rc is not None:
-                raise RuntimeError(
-                    f"rcssservermj exited early (code={rc}) on server port {port}, monitor port {mport}"
-                )
+    @staticmethod
+    def _pid_rss_mb(pid):
+        try:
+            with open(f"/proc/{pid}/status", "r", encoding="utf-8") as f:
+                for line in f:
+                    if line.startswith("VmRSS:"):
+                        parts = line.split()
+                        if len(parts) >= 2:
+                            # VmRSS is kB
+                            return float(parts[1]) / 1024.0
+        except (FileNotFoundError, ProcessLookupError, PermissionError, OSError):
+            return 0.0
+        return 0.0
 
-            self.rcss_processes.append(proc)
+    def _restart_server_at_index(self, idx, reason):
+        port, mport, cmd, render_arg, realtime_arg = self._server_specs[idx]
+        old_proc = self.rcss_processes[idx]
+        try:
+            old_proc.terminate()
+            old_proc.wait(timeout=1.0)
+        except Exception:
+            try:
+                old_proc.kill()
+            except Exception:
+                pass
+
+        new_proc = self._spawn_server(port, mport, cmd, render_arg, realtime_arg)
+        self.rcss_processes[idx] = new_proc
+        print(
+            f"[ServerWatchdog] Restarted server idx={idx} port={port} monitor={mport} reason={reason}"
+        )
+
+    def _watchdog_loop(self):
+        while not self._watchdog_stop.wait(self.WATCHDOG_INTERVAL_SEC):
+            with self._watchdog_lock:
+                for i, proc in enumerate(self.rcss_processes):
+                    rc = proc.poll()
+                    if rc is not None:
+                        self._restart_server_at_index(i, f"exited:{rc}")
+                        continue
+
+                    rss_mb = self._pid_rss_mb(proc.pid)
+                    if rss_mb > self.WATCHDOG_RSS_MB_LIMIT:
+                        self._restart_server_at_index(i, f"rss_mb:{rss_mb:.1f}")
 
     def check_running_servers(self, psutil, first_server_p, first_monitor_p, n_servers):
         ''' Check if any server is running on chosen ports '''
@@ -78,6 +140,9 @@ class Server():
                     return
 
     def kill(self):
+        self._watchdog_stop.set()
+        if self._watchdog_thread is not None:
+            self._watchdog_thread.join(timeout=1.0)
         for p in self.rcss_processes:
             p.kill()
         print(f"Killed {self.n_servers} rcssservermj processes starting at {self.first_server_p}")

scripts/commons/Train_Base.py

@@ -6,7 +6,7 @@ from scripts.commons.UI import UI
 from shutil import copy
 from stable_baselines3 import PPO
 from stable_baselines3.common.base_class import BaseAlgorithm
-from stable_baselines3.common.callbacks import EvalCallback, CheckpointCallback, CallbackList, BaseCallback, StopTrainingOnNoModelImprovement
+from stable_baselines3.common.callbacks import EvalCallback, CheckpointCallback, CallbackList, BaseCallback
 from typing import Callable
 # from world.world import World
 from xml.dom import minidom
@@ -266,28 +266,11 @@ class Train_Base():
 
         evaluate = bool(eval_env is not None and eval_freq is not None)
 
-        # Optional early stop: stop training when eval reward does not improve for N eval rounds.
-        no_improve_evals = int(os.environ.get("GYM_CPU_EARLY_STOP_NO_IMPROVE_EVALS", "0"))
-        min_evals_before_stop = int(os.environ.get("GYM_CPU_EARLY_STOP_MIN_EVALS", "6"))
-        stop_on_no_improve = None
-        if evaluate and no_improve_evals > 0:
-            stop_on_no_improve = StopTrainingOnNoModelImprovement(
-                max_no_improvement_evals=no_improve_evals,
-                min_evals=min_evals_before_stop,
-                verbose=1,
-            )
-
         # Create evaluation callback
-        eval_callback = None if not evaluate else EvalCallback(
-            eval_env,
-            n_eval_episodes=eval_eps,
-            eval_freq=eval_freq,
-            log_path=path,
-            best_model_save_path=path,
-            deterministic=True,
-            render=False,
-            callback_after_eval=stop_on_no_improve,
-        )
+        eval_callback = None if not evaluate else EvalCallback(eval_env, n_eval_episodes=eval_eps, eval_freq=eval_freq,
+                                                               log_path=path,
+                                                               best_model_save_path=path, deterministic=True,
+                                                               render=False)
 
         # Create custom callback to display evaluations
         custom_callback = None if not evaluate else Cyclic_Callback(eval_freq,

scripts/commons/UI.py

@@ -1,302 +0,0 @@
-from itertools import zip_longest
-from math import inf
-import math
-import numpy as np
-import shutil
-
-class UI():
-    console_width = 80
-    console_height = 24
-    
-    @staticmethod
-    def read_particle(prompt, str_options, dtype=str, interval=[-inf,inf]):
-        ''' 
-        Read particle from user from a given dtype or from a str_options list
-
-        Parameters
-        ----------
-        prompt : `str`
-            prompt to show user before reading input
-        str_options : `list`
-            list of str options (in addition to dtype if dtype is not str)
-        dtype : `class`
-            if dtype is str, then user must choose a value from str_options, otherwise it can also send a dtype value
-        interval : `list`
-            [>=min,<max] interval for numeric dtypes
-        
-        Returns
-        -------
-        choice : `int` or dtype
-            index of str_options (int) or value (dtype)
-        is_str_option : `bool`
-            True if `choice` is an index from str_options
-        '''
-        # Check if user has no choice
-        if dtype is str and len(str_options) == 1:
-            print(prompt, str_options[0], sep="")
-            return 0, True
-        elif dtype is int and interval[0] == interval[1]-1:
-            print(prompt, interval[0], sep="")
-            return interval[0], False
-
-        while True:
-            inp = input(prompt)
-            if inp in str_options: 
-                return str_options.index(inp), True
-
-            if dtype is not str:
-                try:
-                    inp = dtype(inp)
-                    if inp >= interval[0] and inp < interval[1]:
-                        return inp, False
-                except:
-                    pass
-            
-            print("Error: illegal input! Options:", str_options, f" or  {dtype}" if dtype != str else "")
-
-    @staticmethod
-    def read_int(prompt, min, max):
-        ''' 
-        Read int from user in a given interval
-        :param prompt: prompt to show user before reading input
-        :param min: minimum input (inclusive)
-        :param max: maximum input (exclusive)
-        :return: choice
-        '''
-        while True:
-            inp = input(prompt)
-            try:
-                inp = int(inp)
-                assert inp >= min and inp < max
-                return inp
-            except:
-                print(f"Error: illegal input! Choose number between {min} and {max-1}")
-
-    @staticmethod
-    def print_table(data, titles=None, alignment=None, cols_width=None, cols_per_title=None, margins=None, numbering=None, prompt=None):
-        '''
-        Print table
-        
-        Parameters
-        ----------
-        data : `list`
-            list of columns, where each column is a list of items
-        titles : `list`
-            list of titles for each column, default is `None` (no titles)
-        alignment : `list`
-            list of alignments per column (excluding titles), default is `None` (left alignment for all cols)
-        cols_width : `list`
-            list of widths per column, default is `None` (fit to content)
-            Positive values indicate a fixed column width
-            Zero indicates that the column will fit its content
-        cols_per_title : `list`
-            maximum number of subcolumns per title, default is `None` (1 subcolumn per title)
-        margins : `list`
-            number of added leading and trailing spaces per column, default is `None` (margin=2 for all columns)
-        numbering : `list`
-            list of booleans per columns, indicating whether to assign numbers to each option
-        prompt : `str`
-            the prompt string, if given, is printed after the table before reading input
-
-        Returns
-        -------
-        index : `int`
-            returns global index of selected item (relative to table)
-        col_index : `int`
-            returns local index of selected item (relative to column)
-        column : `int`
-            returns number of column of selected item (starts at 0)
-        * if `numbering` or `prompt` are `None`, `None` is returned
-        
-
-        Example
-        -------
-        titles = ["Name","Age"]
-        data = [[John,Graciete], [30,50]]
-        alignment = ["<","^"]               # 1st column is left-aligned, 2nd is centered
-        cols_width = [10,5]                # 1st column's width=10, 2nd column's width=5
-        margins = [3,3]                    
-        numbering = [True,False]           # prints: [0-John,1-Graciete][30,50]
-        prompt = "Choose a person:"
-        '''
-        
-        #--------------------------------------------- parameters
-        cols_no = len(data)
-
-        if alignment is None:
-            alignment = ["<"]*cols_no
-
-        if cols_width is None:
-            cols_width = [0]*cols_no
-
-        if numbering is None:
-            numbering = [False]*cols_no
-            any_numbering = False
-        else:
-            any_numbering = True
-
-        if margins is None:
-            margins = [2]*cols_no
-
-        # Fit column to content + margin, if required
-        subcol = [] # subcolumn length and widths
-        for i in range(cols_no):
-            subcol.append([[],[]])
-            if cols_width[i] == 0:
-                numbering_width = 4 if numbering[i] else 0
-                if cols_per_title is None or cols_per_title[i] < 2:
-                    cols_width[i] = max([len(str(item))+numbering_width for item in data[i]]) + margins[i]*2
-                else:
-                    subcol[i][0] = math.ceil(len(data[i])/cols_per_title[i]) # subcolumn maximum length
-                    cols_per_title[i] = math.ceil(len(data[i])/subcol[i][0]) # reduce number of columns as needed
-                    cols_width[i] = margins[i]*(1+cols_per_title[i]) - (1 if numbering[i] else 0) # remove one if numbering, same as when printing
-                    for j in range(cols_per_title[i]):
-                        subcol_data_width = max([len(str(item))+numbering_width for item in data[i][j*subcol[i][0]:j*subcol[i][0]+subcol[i][0]]])
-                        cols_width[i] += subcol_data_width     # add subcolumn data width to column width
-                        subcol[i][1].append(subcol_data_width) # save subcolumn data width
-                        
-                if titles is not None: # expand to acomodate titles if needed
-                    cols_width[i] = max(cols_width[i], len(titles[i]) + margins[i]*2  )
-
-        if any_numbering:
-            no_of_items=0
-            cumulative_item_per_col=[0] # useful for getting the local index
-            for i in range(cols_no):
-                assert type(data[i]) == list, "In function 'print_table', 'data' must be a list of lists!"
-
-                if numbering[i]:
-                    data[i] = [f"{n+no_of_items:3}-{d}" for n,d in enumerate(data[i])]
-                    no_of_items+=len(data[i])
-                cumulative_item_per_col.append(no_of_items)
-
-        table_width = sum(cols_width)+cols_no-1
-
-        #--------------------------------------------- col titles
-        print(f'{"="*table_width}')
-        if titles is not None:
-            for i in range(cols_no):
-                print(f'{titles[i]:^{cols_width[i]}}', end='|' if i < cols_no - 1 else '')
-            print()
-            for i in range(cols_no):
-                print(f'{"-"*cols_width[i]}', end='+' if i < cols_no - 1 else '')
-            print()
-
-        #--------------------------------------------- merge subcolumns
-        if cols_per_title is not None:
-            for i,col in enumerate(data):
-                if cols_per_title[i] < 2:
-                    continue
-                for k in range(subcol[i][0]): # create merged items
-                    col[k] = (" "*margins[i]).join( f'{col[item]:{alignment[i]}{subcol[i][1][subcol_idx]}}' 
-                                                    for subcol_idx, item in enumerate(range(k,len(col),subcol[i][0])) )
-                del col[subcol[i][0]:] # delete repeated items
-        
-        #--------------------------------------------- col items
-        for line in zip_longest(*data):       
-            for i,item in enumerate(line):
-                l_margin = margins[i]-1 if numbering[i] else margins[i] # adjust margins when there are numbered options
-                item = "" if item is None else f'{" "*l_margin}{item}{" "*margins[i]}' # add margins
-                print(f'{item:{alignment[i]}{cols_width[i]}}', end='')
-                if i < cols_no - 1:
-                    print(end='|')
-            print(end="\n")
-        print(f'{"="*table_width}')
-
-        #--------------------------------------------- prompt
-        if prompt is None:
-            return None
-
-        if not any_numbering:
-            print(prompt)
-            return None
-
-        index = UI.read_int(prompt, 0, no_of_items)
-
-        for i,n in enumerate(cumulative_item_per_col):
-            if index < n:
-                return index, index-cumulative_item_per_col[i-1], i-1
-
-        raise ValueError('Failed to catch illegal input')
-
-
-    @staticmethod
-    def print_list(data, numbering=True, prompt=None, divider=" | ", alignment="<", min_per_col=6):
-        '''
-        Print list - prints list, using as many columns as possible
-        
-        Parameters
-        ----------
-        data : `list`
-            list of items
-        numbering : `bool`
-            assigns number to each option
-        prompt : `str`
-            the prompt string, if given, is printed after the table before reading input
-        divider : `str`
-            string that divides columns
-        alignment : `str`
-            f-string style alignment ( '<', '>', '^' )
-        min_per_col : int
-            avoid splitting columns with fewer items
-        
-        Returns
-        -------
-        item : `int`, item
-            returns tuple with global index of selected item and the item object,
-            or `None` (if `numbering` or `prompt` are `None`)
-
-        '''
-        
-        WIDTH = shutil.get_terminal_size()[0]
-
-        data_size = len(data)   
-        items = []
-        items_len = []
-
-        #--------------------------------------------- Add numbers, margins and divider
-        for i in range(data_size):
-            number = f"{i}-" if numbering else ""
-            items.append( f"{divider}{number}{data[i]}" )
-            items_len.append( len(items[-1]) )
-
-        max_cols = np.clip((WIDTH+len(divider)) // min(items_len),1,math.ceil(data_size/max(min_per_col,1))) # width + len(divider) because it is not needed in last col
-
-        #--------------------------------------------- Check maximum number of columns, considering content width (min:1)
-        for i in range(max_cols,0,-1):
-            cols_width = []
-            cols_items = []
-            table_width = 0
-            a,b = divmod(data_size,i)
-            for col in range(i):
-                start = a*col + min(b,col)
-                end = start+a+(1 if col<b else 0)
-                cols_items.append( items[start:end] )
-                col_width = max(items_len[start:end])
-                cols_width.append( col_width )
-                table_width += col_width
-            if table_width <= WIDTH+len(divider):
-                break
-        table_width -= len(divider)
-        
-        #--------------------------------------------- Print columns
-        print("="*table_width)
-        for row in range(math.ceil(data_size / i)):
-            for col in range(i):
-                content = cols_items[col][row] if len(cols_items[col]) > row else divider # print divider when there are no items
-                if col == 0:
-                    l = len(divider)
-                    print(end=f"{content[l:]:{alignment}{cols_width[col]-l}}")  # remove divider from 1st col
-                else:
-                    print(end=f"{content    :{alignment}{cols_width[col]  }}") 
-            print()  
-        print("="*table_width)
-
-        #--------------------------------------------- Prompt
-        if prompt is None:
-            return None
-
-        if numbering is None:
-            return None
-        else:
-            idx = UI.read_int( prompt, 0, data_size )
-            return idx, data[idx]

scripts/gyms/Walk.py

@@ -1,853 +0,0 @@
-import os
-import numpy as np
-import math
-import time
-from time import sleep
-from random import random
-from random import uniform
-from itertools import count
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.monitor import Monitor
-from stable_baselines3.common.vec_env import SubprocVecEnv, DummyVecEnv
-
-import gymnasium as gym
-from gymnasium import spaces
-
-from scripts.commons.Train_Base import Train_Base
-from scripts.commons.Server import Server as Train_Server
-
-from agent.base_agent import Base_Agent
-from utils.math_ops import MathOps
-
-from scipy.spatial.transform import Rotation as R
-
-'''
-Objective:
-Learn how to run forward using step primitive
-----------
-- class Basic_Run: implements an OpenAI custom gym
-- class Train:  implements algorithms to train a new model or test an existing model
-'''
-
-
-class WalkEnv(gym.Env):
-    def __init__(self, ip, server_p) -> None:
-
-        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
-        self.Player = player = Base_Agent(
-            team_name="Gym",
-            number=1,
-            host=ip,
-            port=server_p
-        )
-        self.robot_type = self.Player.robot
-        self.step_counter = 0  # to limit episode size
-        self.force_play_on = True
-
-        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
-        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
-        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
-        self.isfallen = False
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.debug_every_n_steps = 5
-        self.enable_debug_joint_status = False
-        self.reward_debug_interval_sec = float(os.environ.get("GYM_CPU_REWARD_DEBUG_INTERVAL_SEC", "600"))
-        self.reward_debug_burst_steps = int(os.environ.get("GYM_CPU_REWARD_DEBUG_BURST_STEPS", "10"))
-        self._reward_debug_last_time = time.time()
-        self._reward_debug_steps_left = 0
-        self.calibrate_nominal_from_neutral = True
-        self.auto_calibrate_train_sim_flip = True
-        self.nominal_calibrated_once = False
-        self.flip_calibrated_once = False
-        self._target_hz = 0.0
-        self._target_dt = 0.0
-        self._last_sync_time = None
-        target_hz_env = 0
-        if target_hz_env:
-            try:
-                self._target_hz = float(target_hz_env)
-            except ValueError:
-                self._target_hz = 0.0
-        if self._target_hz > 0.0:
-            self._target_dt = 1.0 / self._target_hz
-
-        # State space
-        # 原始观测大小: 78
-        obs_size = 78
-        self.obs = np.zeros(obs_size, np.float32)
-        self.observation_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(obs_size,),
-            dtype=np.float32
-        )
-
-        action_dim = len(self.Player.robot.ROBOT_MOTORS)
-        self.no_of_actions = action_dim
-        self.action_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(action_dim,),
-            dtype=np.float32
-        )
-
-        # 中立姿态
-        self.joint_nominal_position = np.array(
-            [
-                0.0,  # 0: Head_yaw (he1)
-                0.0,  # 1: Head_pitch (he2)
-                0.0,  # 2: Left_Shoulder_Pitch (lae1)
-                0.0,  # 3: Left_Shoulder_Roll (lae2)
-                0.0,  # 4: Left_Elbow_Pitch (lae3)
-                0.0,  # 5: Left_Elbow_Yaw (lae4)
-                0.0,  # 6: Right_Shoulder_Pitch (rae1)
-                0.0,  # 7: Right_Shoulder_Roll (rae2)
-                0.0,  # 8: Right_Elbow_Pitch (rae3)
-                0.0,  # 9: Right_Elbow_Yaw (rae4)
-                0.0,  # 10: Waist (te1)
-                0.0,  # 11: Left_Hip_Pitch (lle1)
-                0.0,  # 12: Left_Hip_Roll (lle2)
-                1.0,  # 13: Left_Hip_Yaw (lle3)
-                0.0,  # 14: Left_Knee_Pitch (lle4)
-                0.0,  # 15: Left_Ankle_Pitch (lle5)
-                0.0,  # 16: Left_Ankle_Roll (lle6)
-                0.0,  # 17: Right_Hip_Pitch (rle1)
-                0.0,  # 18: Right_Hip_Roll (rle2)
-                1.0,  # 19: Right_Hip_Yaw (rle3)
-                0.0,  # 20: Right_Knee_Pitch (rle4)
-                0.0,  # 21: Right_Ankle_Pitch (rle5)
-                0.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-        self.joint_nominal_position = np.zeros(self.no_of_actions)
-        self.train_sim_flip = np.array(
-            [
-                1.0,  # 0: Head_yaw (he1)
-                -1.0,  # 1: Head_pitch (he2)
-                1.0,  # 2: Left_Shoulder_Pitch (lae1)
-                -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                -1.0,  # 4: Left_Elbow_Pitch (lae3)
-                1.0,  # 5: Left_Elbow_Yaw (lae4)
-                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                -1.0,  # 7: Right_Shoulder_Roll (rae2)
-                1.0,  # 8: Right_Elbow_Pitch (rae3)
-                1.0,  # 9: Right_Elbow_Yaw (rae4)
-                1.0,  # 10: Waist (te1)
-                1.0,  # 11: Left_Hip_Pitch (lle1)
-                -1.0,  # 12: Left_Hip_Roll (lle2)
-                -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,  # 14: Left_Knee_Pitch (lle4)
-                1.0,  # 15: Left_Ankle_Pitch (lle5)
-                -1.0,  # 16: Left_Ankle_Roll (lle6)
-                -1.0,  # 17: Right_Hip_Pitch (rle1)
-                -1.0,  # 18: Right_Hip_Roll (rle2)
-                -1.0,  # 19: Right_Hip_Yaw (rle3)
-                -1.0,  # 20: Right_Knee_Pitch (rle4)
-                -1.0,  # 21: Right_Ankle_Pitch (rle5)
-                -1.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-
-        self.scaling_factor = 0.3
-        # self.scaling_factor = 1
-
-        # Encourage a minimum lateral stance so the policy avoids feet overlap.
-        self.min_stance_rad = 0.10
-
-        # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = False
-        self.reset_beam_yaw_range_deg = float(os.environ.get("GYM_CPU_RESET_BEAM_YAW_RANGE_DEG", "180"))
-        self.reset_target_bearing_range_deg = float(os.environ.get("GYM_CPU_RESET_TARGET_BEARING_RANGE_DEG", "120"))
-        self.reset_target_distance_min = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MIN", "1.2"))
-        self.reset_target_distance_max = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MAX", "2.8"))
-        if self.reset_target_distance_min > self.reset_target_distance_max:
-            self.reset_target_distance_min, self.reset_target_distance_max = (
-                self.reset_target_distance_max,
-                self.reset_target_distance_min,
-            )
-        self.reset_joint_noise_rad = 0.025
-        self.reset_perturb_steps = 4
-        self.reset_recover_steps = 8
-
-        self.reward_smoothness_scale = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_SCALE", "0.06"))
-        self.reward_smoothness_cap = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_CAP", "0.45"))
-        self.reward_head_toward_bonus = float(os.environ.get("GYM_CPU_REWARD_HEAD_TOWARD_BONUS", "1"))
-
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Track previous position
-        self.last_yaw_error = None
-        self.Player.server.connect()
-        # sleep(2.0)  # Longer wait for connection to establish completely
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-        self.start_time = time.time()
-
-    def _reconnect_server(self):
-        try:
-            self.Player.server.shutdown()
-        except Exception:
-            pass
-
-        self.Player.server.connect()
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-
-    def _safe_receive_world_update(self, retries=1):
-        last_exc = None
-        for attempt in range(retries + 1):
-            try:
-                self.Player.server.receive()
-                self.Player.world.update()
-                return
-            except (ConnectionResetError, OSError) as exc:
-                last_exc = exc
-                if attempt >= retries:
-                    raise
-                self._reconnect_server()
-        if last_exc is not None:
-            raise last_exc
-
-    def debug_log(self, message):
-        print(message)
-        try:
-            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
-            with open(log_path, "a", encoding="utf-8") as f:
-                f.write(message + "\n")
-        except OSError:
-            pass
-
-    @staticmethod
-    def _wrap_to_pi(angle_rad: float) -> float:
-        return (angle_rad + math.pi) % (2.0 * math.pi) - math.pi
-
-    def observe(self, init=False):
-
-        """获取当前观测值"""
-        robot = self.Player.robot
-        world = self.Player.world
-
-        # Safety check: ensure data is available
-
-        # 计算目标速度
-        raw_target = self.target_position - world.global_position[:2]
-        velocity = MathOps.rotate_2d_vec(
-            raw_target,
-            -robot.global_orientation_euler[2],
-            is_rad=False
-        )
-
-        # 计算相对方向
-        rel_orientation = MathOps.vector_angle(velocity) * 0.3
-        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
-
-        velocity = np.concatenate([velocity, np.array([rel_orientation])])
-        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
-        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
-
-        # 关节状态
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        qpos_qvel_previous_action = np.concatenate([
-            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
-            radian_joint_speeds / 110.0 * self.train_sim_flip,
-            self.previous_action / 10.0,
-        ])
-
-        # 角速度
-        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
-
-        # 投影的重力方向
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-
-        # 组合观测
-        observation = np.concatenate([
-            qpos_qvel_previous_action,
-            ang_vel,
-            velocity,
-            projected_gravity,
-        ])
-
-        observation = np.clip(observation, -10.0, 10.0)
-        return observation.astype(np.float32)
-
-    def sync(self):
-        ''' Run a single simulation step '''
-        self._safe_receive_world_update(retries=1)
-        self.Player.robot.commit_motor_targets_pd()
-        self.Player.server.send()
-        if self._target_dt > 0.0:
-            now = time.time()
-            if self._last_sync_time is None:
-                self._last_sync_time = now
-                return
-            elapsed = now - self._last_sync_time
-            remaining = self._target_dt - elapsed
-            if remaining > 0.0:
-                time.sleep(remaining)
-                now = time.time()
-            self._last_sync_time = now
-
-    def debug_joint_status(self):
-        robot = self.Player.robot
-        actual_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        target_joint_positions = getattr(
-            self,
-            'target_joint_positions',
-            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
-        )
-        joint_error = actual_joint_positions - target_joint_positions
-        leg_slice = slice(11, None)
-
-        self.debug_log(
-            "[WalkDebug] "
-            f"step={self.step_counter} "
-            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
-            f"target_xy={np.round(self.target_position, 3).tolist()} "
-            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
-            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
-            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
-            f"fallen={self.Player.world.global_position[2] < 0.3}"
-        )
-        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
-
-    def reset(self, seed=None, options=None):
-        '''
-        Reset and stabilize the robot
-        Note: for some behaviors it would be better to reduce stabilization or add noise
-        '''
-        r = self.Player.robot
-        super().reset(seed=seed)
-        if seed is not None:
-            np.random.seed(seed)
-
-        target_distance = np.random.uniform(self.reset_target_distance_min, self.reset_target_distance_max)
-        target_bearing_deg = np.random.uniform(-self.reset_target_bearing_range_deg, self.reset_target_bearing_range_deg)
-
-        self.step_counter = 0
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
-        self.last_yaw_error = None
-        self.walk_cycle_step = 0
-        self._reward_debug_steps_left = 0
-
-        # 随机 beam 目标位置和朝向，增加训练多样性
-        beam_x = (random() - 0.5) * 10
-        beam_y = (random() - 0.5) * 10
-        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
-
-        for _ in range(5):
-            self._safe_receive_world_update(retries=2)
-            self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
-            self.Player.server.send()
-
-        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
-        finished_count = 0
-        for _ in range(50):
-            finished = self.Player.skills_manager.execute("Neutral")
-            self.sync()
-            if finished:
-                finished_count += 1
-                if finished_count >= 20:  # 假设需要连续20次完成才算成功
-                    break
-
-        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-            # Perturb waist + lower body only (10:), keep head/arms stable.
-            perturb_action[10:] = np.random.uniform(
-                -self.reset_joint_noise_rad,
-                self.reset_joint_noise_rad,
-                size=(self.no_of_actions - 10,)
-            )
-
-            for _ in range(self.reset_perturb_steps):
-                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-            for i in range(self.reset_recover_steps):
-                # Linearly fade perturbation to help policy start from near-neutral.
-                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-        # memory variables
-        self.sync()  
-        self.initial_position = np.array(self.Player.world.global_position[:2])
-        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions, np.float32)
-        # Randomize global target bearing so policy must learn to rotate toward it first.
-        heading_deg = float(r.global_orientation_euler[2])
-        target_offset = MathOps.rotate_2d_vec(
-            np.array([target_distance, 0.0]),
-            heading_deg + target_bearing_deg,
-            is_rad=False,
-        )
-        point1 = self.initial_position + target_offset
-        self.point_list = [point1]
-        self.target_position = self.point_list[self.waypoint_index]
-        self.initial_height = self.Player.world.global_position[2]
-
-        return self.observe(True), {}
-
-    def render(self, mode='human', close=False):
-        return
-
-    
-    def compute_reward(self, previous_pos, current_pos, action):
-        height = float(self.Player.world.global_position[2])
-        robot = self.Player.robot
-
-
-        joint_pos_rad = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        joint_speed_rad = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-        tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        ang_vel = np.deg2rad(robot.gyroscope)
-        rp_ang_vel_mag = float(np.linalg.norm(ang_vel[:2]))
-
-        is_fallen = height < 0.55
-        if is_fallen:
-            # remain = max(0, 800 - self.step_counter)
-            # return -8.0 - 0.01 * remain
-            return -20.0
-        
-
-        if np.linalg.norm(current_pos - previous_pos) > 0.005:
-            position_penalty = -3 * float(np.linalg.norm(current_pos - previous_pos))
-        else:
-            position_penalty = 0.0
-
-
-        # Turn-to-target shaping.
-        to_target = self.target_position - current_pos
-        dist_to_target = float(np.linalg.norm(to_target))
-        if dist_to_target > 1e-6:
-            target_yaw = math.atan2(float(to_target[1]), float(to_target[0]))
-        else:
-            target_yaw = 0.0
-
-        robot_yaw = math.radians(float(robot.global_orientation_euler[2]))
-        yaw_error = target_yaw - robot_yaw
-
-        # Main heading objective: face the target direction.
-        # heading_align_reward = 1.0 * math.cos(yaw_error)
-
-        abs_yaw_error = abs(yaw_error)
-        alive_bonus = 2.0 * max(0.0, 1.0 - abs_yaw_error / math.pi)
-        head_toward_bonus = self.reward_head_toward_bonus if abs_yaw_error < math.radians(4.0) else 0.0
-
-        if self.last_yaw_error is None:
-            heading_progress_reward = 0.0
-        else:
-            prev_abs_yaw_error = abs(self.last_yaw_error)
-            yaw_err_delta = prev_abs_yaw_error - abs_yaw_error
-            progress_gate = 1.0 if abs_yaw_error > math.radians(4.0) else 0.0
-            heading_progress_reward =   progress_gate * yaw_err_delta
-            heading_progress_reward = float(np.clip(heading_progress_reward, -1, 1))
-        self.last_yaw_error = yaw_error
-
-        # action_penalty = -0.01 * float(np.linalg.norm(action))
-        smoothness_penalty = -0.05 * float(np.linalg.norm(action - self.last_action_for_reward))
-
-        posture_penalty = -0.6 * tilt_mag
-        # Penalize roll/pitch rotational shake but do not penalize yaw turning directly.
-        ang_vel_penalty = -0.06 * rp_ang_vel_mag
-
-        joint_pos = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        ) * self.train_sim_flip
-        left_hip_roll = float(joint_pos[12])
-        right_hip_roll = float(joint_pos[18])
-        left_hip_pitch = float(joint_pos[11])
-        right_hip_pitch = float(joint_pos[17])
-
-        left_ankle_roll = float(joint_pos[16])
-        right_ankle_roll = float(joint_pos[22])
-
-        max_leg_roll = 0.2  # 防止劈叉姿势
-        split_penalty = -0.8 * max(0.0, (-left_hip_roll + right_hip_roll - 2 * max_leg_roll) / max_leg_roll)
-        left_hip_yaw = float(joint_pos[13])
-        right_hip_yaw = float(joint_pos[19])
-
-        min_leg_separation = 0.05  # 最小腿间距（防止贴得太近）
-        # 惩罚腿过分靠拢（内收）- 基于两腿间距
-        leg_separation = -left_hip_roll + right_hip_roll
-        inward_penalty = -0.25 * max(0.0, (min_leg_separation - leg_separation) / min_leg_separation)
-        
-        
-        # 脚踝roll角度检测：防止过度外翻或内翻
-        max_ankle_roll = 0.15  # 最大允许的脚踝roll角度
-        
-        # 惩罚脚踝过度外翻/内翻（绝对值过大）
-        ankle_roll_penalty = -0.5 * max(0.0, (abs(left_ankle_roll) + abs(right_ankle_roll) - 2 * max_ankle_roll) / max_ankle_roll)
-        
-        # 惩罚两脚踝roll方向相反（不稳定姿势）
-        ankle_roll_cross_penalty = -0.3 * max(0.0, -(left_ankle_roll * right_ankle_roll))
-
-       # 分别惩罚左右大腿过度转动
-        max_hip_yaw = 0.5  # 最大允许的yaw角度
-        left_hip_yaw_penalty = -0.4 * max(0.0, abs(left_hip_yaw) - max_hip_yaw)
-        right_hip_yaw_penalty = -0.4 * max(0.0, abs(right_hip_yaw) - max_hip_yaw)
-        # 智能交叉腿惩罚：只在站立时惩罚，转身时允许交叉腿
-        yaw_rate = float(np.deg2rad(robot.gyroscope[2]))
-        yaw_rate_abs = abs(yaw_rate)
-
-        # 当转身速度较小时才惩罚交叉腿（站立状态）
-        cross_leg_gate = max(0.0, 1.0 - yaw_rate_abs / math.radians(8.0))
-        hip_yaw_cross_penalty = -1.0 * cross_leg_gate * max(0.0, -(left_hip_yaw * right_hip_yaw)) if left_hip_yaw > 0 and right_hip_yaw < 0 else 0.0
-
-        # Torso-lower-body linkage: reward coordinated turning, punish waist-only spinning.
-        waist_speed = abs(float(joint_speed_rad[10]))
-        lower_body_speed = float(np.mean(np.abs(joint_speed_rad[11:23])))
-        lower_body_follow_ratio = lower_body_speed / (waist_speed + 1e-4)
-        linkage_reward = 0.24 * min(1.0, lower_body_follow_ratio) * min(1.0, waist_speed / 1.2)
-        waist_only_turn_penalty = -0.20 * max(0.0, waist_speed - 1.35 * lower_body_speed)
-
-        # Extra posture linkage in yaw joints to avoid decoupled torso twist.
-        waist_yaw = abs(float(joint_pos_rad[10]))
-        hip_yaw_mean = 0.5 * (abs(float(joint_pos_rad[13])) + abs(float(joint_pos_rad[19])))
-        yaw_link_reward = 0.12 * math.exp(-abs(waist_yaw - hip_yaw_mean) / 0.22)
-
-        target_height = self.initial_height
-        height_error =  height - target_height
-        height_error = height - target_height
-        
-        height_penalty = -(math.exp(12*abs(height_error))-1) if height_error > 0.04 else 0
-
-        # # 在 compute_reward 开头附近，添加高度变化率计算
-        # if not hasattr(self, 'last_height'):
-        #     self.last_height = height
-        #     self.last_height_time = self.step_counter  # 可选，用于时间间隔
-        # height_rate = height - self.last_height  # 正为上升，负为下降
-        # self.last_height = height
-
-        # 惩罚高度下降（负变化率）
-        # height_down_penalty = -5.0 * max(0, -height_rate)  # 系数可调，-height_rate 为正表示下降幅度
-
-        # # 在 compute_reward 中
-        # if self.step_counter > 50:
-        #     avg_prev_action = np.mean(self.prev_action_history, axis=0)
-        #     novelty = float(np.linalg.norm(action - avg_prev_action))
-        #     exploration_bonus = 0.05 * novelty
-        # else:
-        #     exploration_bonus = 0
-
-        # self.prev_action_history[self.history_idx] = action
-        # self.history_idx = (self.history_idx + 1) % 50
-
-
-        total = (
-            # progress_reward  + 
-                alive_bonus + 
-                head_toward_bonus +
-                heading_progress_reward +
-                # lateral_penalty +
-                # action_penalty +
-                smoothness_penalty +
-                posture_penalty
-                + ang_vel_penalty
-                + height_penalty
-                + ankle_roll_penalty
-                + ankle_roll_cross_penalty
-                + split_penalty
-                + inward_penalty
-                # + leg_proximity_penalty
-                + left_hip_yaw_penalty
-                + right_hip_yaw_penalty
-                + hip_yaw_cross_penalty
-                + position_penalty
-                # + linkage_reward
-                # + waist_only_turn_penalty
-                # + yaw_link_reward
-                # + stance_collapse_penalty
-                # + hip_yaw_yaw_cross_penalty
-                # + stance_collapse_penalty
-                #  + cross_leg_penalty
-                #  + exploration_bonus
-                # + height_down_penalty
-                 )
-        # print(height_error, height_penalty)
-        
-        now = time.time()
-        if self.reward_debug_interval_sec > 0 and now - self._reward_debug_last_time >= self.reward_debug_interval_sec:
-            self._reward_debug_last_time = now
-            self._reward_debug_steps_left = max(1, self.reward_debug_burst_steps)
-
-        if self._reward_debug_steps_left > 0:
-            self._reward_debug_steps_left -= 1
-            self.debug_log(
-                f"height_penalty:{height_penalty:.4f},"
-                f"smoothness_penalty:{smoothness_penalty:.4f},"
-                f"posture_penalty:{posture_penalty:.4f},"
-                f"heading_progress_reward:{heading_progress_reward:.4f},"
-                # f"stance_collapse_penalty:{stance_collapse_penalty:.4f},"
-                # f"cross_leg_penalty:{cross_leg_penalty:.4f},"
-                f"ang_vel_penalty:{ang_vel_penalty:.4f},"
-                f"split_penalty:{split_penalty:.4f},"
-                f"ankle_roll_penalty:{ankle_roll_penalty:.4f},"
-                f"ankle_roll_cross_penalty:{ankle_roll_cross_penalty:.4f},"
-                f"left_hip_yaw_penalty:{left_hip_yaw_penalty:.4f},"
-                f"right_hip_yaw_penalty:{right_hip_yaw_penalty:.4f},"
-                f"hip_yaw_cross_penalty:{hip_yaw_cross_penalty:.4f},"
-                f"inward_penalty:{inward_penalty:.4f},"
-                f"position_penalty:{position_penalty:.4f},"
-                # f"linkage_reward:{linkage_reward:.4f},"
-                # f"waist_only_turn_penalty:{waist_only_turn_penalty:.4f},"
-                # f"yaw_link_reward:{yaw_link_reward:.4f}"
-                # f"leg_proximity_penalty:{leg_proximity_penalty:.4f},"
-
-                # f"stance_collapse_penalty:{stance_collapse_penalty:.4f},"
-                # f"hip_yaw_yaw_cross_penalty:{hip_yaw_yaw_cross_penalty:.4f},"
-                #   f"height_down_penalty:{height_down_penalty:.4f}",
-                #   f"exploration_bonus:{exploration_bonus:.4f}"
-                f"alive_bonus:{alive_bonus:.4f},"
-                f"abs_yaw_error:{abs_yaw_error:.4f}"
-                f"total:{total:.4f}"
-                )
-        # print(f"abs_yaw_error:{abs_yaw_error:.4f}")
-        return total
-
-
-
-    def step(self, action):
-
-        r = self.Player.robot
-        max_action_delta =  0.5# Limit how much the action can change from the previous step to encourage smoother motions.
-        if self.previous_action is not None:
-            action = np.clip(action, self.previous_action - max_action_delta, self.previous_action + max_action_delta)
-        action[0:2] = 0
-        action[3] = 4
-        action[7] = -4
-        action[2] = 0
-        action[6] = 0
-        action[4] = 0
-        action[5] = -5
-        action[8] = 0
-        action[9] = 5
-        action[10] = 0
-        action[11] = np.clip(action[11], -0.7, 0.7)
-        action[17] = np.clip(action[17], -0.7, 0.7)
-        # action[12] = -1.0
-        # action[18] = 1.0
-        # action[13] = -1.0
-        # action[19] = 1.0
-        self.previous_action = action.copy()
-
-        self.target_joint_positions = (
-                # self.joint_nominal_position +
-                 self.scaling_factor * action
-        )
-        self.target_joint_positions *= self.train_sim_flip
-
-        for idx, target in enumerate(self.target_joint_positions):
-            r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=80, kd=4.67
-            )
-
-        self.previous_action = action.copy()
-
-        self.sync()  # run simulation step
-        self.step_counter += 1
-
-        if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
-            self.debug_joint_status()
-
-        current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
-
-        if self.step_counter % 10 == 0:
-            self.previous_pos = current_pos.copy()
-
-        # Compute reward based on movement from previous step
-        reward = self.compute_reward(self.previous_pos, current_pos, action)
-
-        self.last_action_for_reward = action.copy()
-
-        # Fall detection and penalty
-        is_fallen = self.Player.world.global_position[2] < 0.55
-
-        # terminal state: the robot is falling or timeout
-        terminated = is_fallen or self.step_counter > 800 or self.route_completed
-        truncated = False
-
-        return self.observe(), reward, terminated, truncated, {}
-
-
-class Train(Train_Base):
-    def __init__(self, script) -> None:
-        super().__init__(script)
-
-    def train(self, args):
-
-        # --------------------------------------- Learning parameters
-        n_envs = int(os.environ.get("GYM_CPU_N_ENVS", "20"))
-        if n_envs < 1:
-            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
-        server_warmup_sec = float(os.environ.get("GYM_CPU_SERVER_WARMUP_SEC", "3.0"))
-        n_steps_per_env = int(os.environ.get("GYM_CPU_TRAIN_STEPS_PER_ENV", "512"))  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = int(os.environ.get("GYM_CPU_TRAIN_BATCH_SIZE", "512"))  # should be a factor of (n_steps_per_env * n_envs)
-        total_steps = 30000000
-        learning_rate = float(os.environ.get("GYM_CPU_TRAIN_LR", "3e-4"))
-        folder_name = f'Turn_R{self.robot_type}'
-        model_path = f'./scripts/gyms/logs/{folder_name}/'
-
-        print(f"Model path: {model_path}")
-        print(f"Using {n_envs} parallel environments")
-
-        # --------------------------------------- Run algorithm
-        def init_env(i_env, monitor=False):
-            def thunk():
-                env = WalkEnv(self.ip, self.server_p + i_env)
-                if monitor:
-                    env = Monitor(env)
-                return env
-
-            return thunk
-
-        server_log_dir = os.path.join(model_path, "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1, no_render=True, no_realtime=True)  # include 1 extra server for testing
-
-        # Wait for servers to start
-        print(f"Starting {n_envs + 1} rcssservermj servers...")
-        if server_warmup_sec > 0:
-            print(f"Waiting {server_warmup_sec:.1f}s for server warmup...")
-            sleep(server_warmup_sec)
-        print("Servers started, creating environments...")
-
-        env = SubprocVecEnv([init_env(i, monitor=True) for i in range(n_envs)], start_method="spawn")
-        # Use single-process eval env to avoid extra subprocess fragility during callback evaluation.
-        eval_env = DummyVecEnv([init_env(n_envs, monitor=True)])
-
-        try:
-            # Custom policy network architecture
-            policy_kwargs = dict(
-                net_arch=dict(
-                    pi=[512, 256, 128],  # Policy network: 3 layers
-                    vf=[512, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
-            )
-
-            if "model_file" in args:  # retrain
-                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
-                                 batch_size=minibatch_size, learning_rate=learning_rate)
-            else:  # train new model
-                model = PPO(
-                    "MlpPolicy",
-                    env=env,
-                    verbose=1,
-                    n_steps=n_steps_per_env,
-                    batch_size=minibatch_size,
-                    learning_rate=learning_rate,
-                    device="cpu",
-                    policy_kwargs=policy_kwargs,
-                    ent_coef=float(os.environ.get("GYM_CPU_TRAIN_ENT_COEF", "0.05")),  # Entropy coefficient for exploration
-                    clip_range=float(os.environ.get("GYM_CPU_TRAIN_CLIP_RANGE", "0.2")),  # PPO clipping parameter
-                    gae_lambda=0.95,  # GAE lambda
-                    gamma=float(os.environ.get("GYM_CPU_TRAIN_GAMMA", "0.95")), # Discount factor
-                    # target_kl=0.03,
-                    n_epochs=int(os.environ.get("GYM_CPU_TRAIN_EPOCHS", "5")),
-                    tensorboard_log=f"./scripts/gyms/logs/{folder_name}/tensorboard/"
-                )
-
-            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
-                                          eval_freq=n_steps_per_env * 20, save_freq=n_steps_per_env * 20, eval_eps=7,
-                                          backup_env_file=__file__)
-        except KeyboardInterrupt:
-            sleep(1)  # wait for child processes
-            print("\nctrl+c pressed, aborting...\n")
-            servers.kill()
-            return
-
-        env.close()
-        eval_env.close()
-        servers.kill()
-
-    def test(self, args):
-
-        # Uses different server and monitor ports
-        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        test_no_render = os.environ.get("GYM_CPU_TEST_NO_RENDER", "0") == "1"
-        test_no_realtime = os.environ.get("GYM_CPU_TEST_NO_REALTIME", "0") == "1"
-
-        server = Train_Server(
-            self.server_p - 1,
-            self.monitor_p,
-            1,
-            no_render=test_no_render,
-            no_realtime=test_no_realtime,
-        )
-        env = WalkEnv(self.ip, self.server_p - 1)
-        model = PPO.load(args["model_file"], env=env)
-
-        try:
-            self.export_model(args["model_file"], args["model_file"] + ".pkl",
-                              False)  # Export to pkl to create custom behavior
-            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
-        except KeyboardInterrupt:
-            print()
-
-        env.close()
-        server.kill()
-
-
-if __name__ == "__main__":
-    from types import SimpleNamespace
-
-    # 创建默认参数
-    script_args = SimpleNamespace(
-        args=SimpleNamespace(
-            i='127.0.0.1',  # Server IP
-            p=3100,  # Server port
-            m=3200,  # Monitor port
-            r=0,  # Robot type
-            t='Gym',  # Team name
-            u=1  # Uniform number
-        )
-    )
-
-    trainer = Train(script_args)
-
-    run_mode = os.environ.get("GYM_CPU_MODE", "train").strip().lower()
-
-    if run_mode == "test":
-        test_model_file = os.environ.get("GYM_CPU_TEST_MODEL", "scripts/gyms/logs/Turn_R0_004/best_model.zip")
-        test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Turn_R0_004/")
-        trainer.test({"model_file": test_model_file, "folder_dir": test_folder})
-    else:
-        retrain_model = os.environ.get("GYM_CPU_TRAIN_MODEL", "").strip()
-        if retrain_model:
-            trainer.train({"model_file": retrain_model})
-        else:
-            trainer.train({})

scripts/gyms/logs/Turn_R0_000/Walk.py

@@ -1,832 +0,0 @@
-import os
-import numpy as np
-import math
-import time
-from time import sleep
-from random import random
-from random import uniform
-from itertools import count
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.monitor import Monitor
-from stable_baselines3.common.vec_env import SubprocVecEnv, DummyVecEnv
-
-import gymnasium as gym
-from gymnasium import spaces
-
-from scripts.commons.Train_Base import Train_Base
-from scripts.commons.Server import Server as Train_Server
-
-from agent.base_agent import Base_Agent
-from utils.math_ops import MathOps
-
-from scipy.spatial.transform import Rotation as R
-
-'''
-Objective:
-Learn how to run forward using step primitive
-----------
-- class Basic_Run: implements an OpenAI custom gym
-- class Train:  implements algorithms to train a new model or test an existing model
-'''
-
-
-class WalkEnv(gym.Env):
-    def __init__(self, ip, server_p) -> None:
-
-        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
-        self.Player = player = Base_Agent(
-            team_name="Gym",
-            number=1,
-            host=ip,
-            port=server_p
-        )
-        self.robot_type = self.Player.robot
-        self.step_counter = 0  # to limit episode size
-        self.force_play_on = True
-
-        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
-        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
-        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
-        self.isfallen = False
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.debug_every_n_steps = 5
-        self.enable_debug_joint_status = False
-        self.reward_debug_interval_sec = float(os.environ.get("GYM_CPU_REWARD_DEBUG_INTERVAL_SEC", "600"))
-        self.reward_debug_burst_steps = int(os.environ.get("GYM_CPU_REWARD_DEBUG_BURST_STEPS", "10"))
-        self._reward_debug_last_time = time.time()
-        self._reward_debug_steps_left = 0
-        self.calibrate_nominal_from_neutral = True
-        self.auto_calibrate_train_sim_flip = True
-        self.nominal_calibrated_once = False
-        self.flip_calibrated_once = False
-        self._target_hz = 0.0
-        self._target_dt = 0.0
-        self._last_sync_time = None
-        target_hz_env = 0
-        if target_hz_env:
-            try:
-                self._target_hz = float(target_hz_env)
-            except ValueError:
-                self._target_hz = 0.0
-        if self._target_hz > 0.0:
-            self._target_dt = 1.0 / self._target_hz
-
-        # State space
-        # 原始观测大小: 78
-        obs_size = 78
-        self.obs = np.zeros(obs_size, np.float32)
-        self.observation_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(obs_size,),
-            dtype=np.float32
-        )
-
-        action_dim = len(self.Player.robot.ROBOT_MOTORS)
-        self.no_of_actions = action_dim
-        self.action_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(action_dim,),
-            dtype=np.float32
-        )
-
-        # 中立姿态
-        self.joint_nominal_position = np.array(
-            [
-                0.0,
-                0.0,
-                0.0,
-                1.4,
-                0.0,
-                -0.4,
-                0.0,
-                -1.4,
-                0.0,
-                0.4,
-                0.0,
-                -0.4,
-                0.0,
-                0.0,
-                0.8,
-                -0.4,
-                0.0,
-                0.4,
-                0.0,
-                0.0,
-                -0.8,
-                0.4,
-                0.0,
-            ]
-        )
-        self.joint_nominal_position = np.zeros(self.no_of_actions)
-        self.train_sim_flip = np.array(
-            [
-                1.0,  # 0: Head_yaw (he1)
-                -1.0,  # 1: Head_pitch (he2)
-                1.0,  # 2: Left_Shoulder_Pitch (lae1)
-                -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                -1.0,  # 4: Left_Elbow_Pitch (lae3)
-                1.0,  # 5: Left_Elbow_Yaw (lae4)
-                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                -1.0,  # 7: Right_Shoulder_Roll (rae2)
-                1.0,  # 8: Right_Elbow_Pitch (rae3)
-                1.0,  # 9: Right_Elbow_Yaw (rae4)
-                1.0,  # 10: Waist (te1)
-                1.0,  # 11: Left_Hip_Pitch (lle1)
-                -1.0,  # 12: Left_Hip_Roll (lle2)
-                -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,  # 14: Left_Knee_Pitch (lle4)
-                1.0,  # 15: Left_Ankle_Pitch (lle5)
-                -1.0,  # 16: Left_Ankle_Roll (lle6)
-                -1.0,  # 17: Right_Hip_Pitch (rle1)
-                -1.0,  # 18: Right_Hip_Roll (rle2)
-                -1.0,  # 19: Right_Hip_Yaw (rle3)
-                -1.0,  # 20: Right_Knee_Pitch (rle4)
-                -1.0,  # 21: Right_Ankle_Pitch (rle5)
-                -1.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-
-        self.scaling_factor = 0.3
-        # self.scaling_factor = 1
-
-        # Encourage a minimum lateral stance so the policy avoids feet overlap.
-        self.min_stance_rad = 0.10
-
-        # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = False
-        self.reset_beam_yaw_range_deg = float(os.environ.get("GYM_CPU_RESET_BEAM_YAW_RANGE_DEG", "180"))
-        self.reset_target_bearing_range_deg = float(os.environ.get("GYM_CPU_RESET_TARGET_BEARING_RANGE_DEG", "45"))
-        self.reset_target_distance_min = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MIN", "1.2"))
-        self.reset_target_distance_max = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MAX", "2.8"))
-        if self.reset_target_distance_min > self.reset_target_distance_max:
-            self.reset_target_distance_min, self.reset_target_distance_max = (
-                self.reset_target_distance_max,
-                self.reset_target_distance_min,
-            )
-        self.reset_joint_noise_rad = 0.025
-        self.reset_perturb_steps = 4
-        self.reset_recover_steps = 8
-
-        self.reward_smoothness_scale = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_SCALE", "0.06"))
-        self.reward_smoothness_cap = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_CAP", "0.45"))
-        self.reward_head_toward_bonus = float(os.environ.get("GYM_CPU_REWARD_HEAD_TOWARD_BONUS", "0.7"))
-
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Track previous position
-        self.last_yaw_error = None
-        self.Player.server.connect()
-        # sleep(2.0)  # Longer wait for connection to establish completely
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-        self.start_time = time.time()
-
-    def _reconnect_server(self):
-        try:
-            self.Player.server.shutdown()
-        except Exception:
-            pass
-
-        self.Player.server.connect()
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-
-    def _safe_receive_world_update(self, retries=1):
-        last_exc = None
-        for attempt in range(retries + 1):
-            try:
-                self.Player.server.receive()
-                self.Player.world.update()
-                return
-            except (ConnectionResetError, OSError) as exc:
-                last_exc = exc
-                if attempt >= retries:
-                    raise
-                self._reconnect_server()
-        if last_exc is not None:
-            raise last_exc
-
-    def debug_log(self, message):
-        print(message)
-        try:
-            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
-            with open(log_path, "a", encoding="utf-8") as f:
-                f.write(message + "\n")
-        except OSError:
-            pass
-
-    @staticmethod
-    def _wrap_to_pi(angle_rad: float) -> float:
-        return (angle_rad + math.pi) % (2.0 * math.pi) - math.pi
-
-    def observe(self, init=False):
-
-        """获取当前观测值"""
-        robot = self.Player.robot
-        world = self.Player.world
-
-        # Safety check: ensure data is available
-
-        # 计算目标速度
-        raw_target = self.target_position - world.global_position[:2]
-        velocity = MathOps.rotate_2d_vec(
-            raw_target,
-            -robot.global_orientation_euler[2],
-            is_rad=False
-        )
-
-        # 计算相对方向
-        rel_orientation = MathOps.vector_angle(velocity) * 0.3
-        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
-
-        velocity = np.concatenate([velocity, np.array([rel_orientation])])
-        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
-        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
-
-        # 关节状态
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        qpos_qvel_previous_action = np.concatenate([
-            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
-            radian_joint_speeds / 110.0 * self.train_sim_flip,
-            self.previous_action / 10.0,
-        ])
-
-        # 角速度
-        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
-
-        # 投影的重力方向
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-
-        # 组合观测
-        observation = np.concatenate([
-            qpos_qvel_previous_action,
-            ang_vel,
-            velocity,
-            projected_gravity,
-        ])
-
-        observation = np.clip(observation, -10.0, 10.0)
-        return observation.astype(np.float32)
-
-    def sync(self):
-        ''' Run a single simulation step '''
-        self._safe_receive_world_update(retries=1)
-        self.Player.robot.commit_motor_targets_pd()
-        self.Player.server.send()
-        if self._target_dt > 0.0:
-            now = time.time()
-            if self._last_sync_time is None:
-                self._last_sync_time = now
-                return
-            elapsed = now - self._last_sync_time
-            remaining = self._target_dt - elapsed
-            if remaining > 0.0:
-                time.sleep(remaining)
-                now = time.time()
-            self._last_sync_time = now
-
-    def debug_joint_status(self):
-        robot = self.Player.robot
-        actual_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        target_joint_positions = getattr(
-            self,
-            'target_joint_positions',
-            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
-        )
-        joint_error = actual_joint_positions - target_joint_positions
-        leg_slice = slice(11, None)
-
-        self.debug_log(
-            "[WalkDebug] "
-            f"step={self.step_counter} "
-            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
-            f"target_xy={np.round(self.target_position, 3).tolist()} "
-            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
-            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
-            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
-            f"fallen={self.Player.world.global_position[2] < 0.3}"
-        )
-        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
-
-    def reset(self, seed=None, options=None):
-        '''
-        Reset and stabilize the robot
-        Note: for some behaviors it would be better to reduce stabilization or add noise
-        '''
-        r = self.Player.robot
-        super().reset(seed=seed)
-        if seed is not None:
-            np.random.seed(seed)
-
-        target_distance = np.random.uniform(self.reset_target_distance_min, self.reset_target_distance_max)
-        target_bearing_deg = np.random.uniform(-self.reset_target_bearing_range_deg, self.reset_target_bearing_range_deg)
-
-        self.step_counter = 0
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
-        self.last_yaw_error = None
-        self.walk_cycle_step = 0
-        self._reward_debug_steps_left = 0
-
-        # 随机 beam 目标位置和朝向，增加训练多样性
-        beam_x = (random() - 0.5) * 10
-        beam_y = (random() - 0.5) * 10
-        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
-
-        for _ in range(5):
-            self._safe_receive_world_update(retries=2)
-            self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
-            self.Player.server.send()
-
-        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
-        finished_count = 0
-        for _ in range(50):
-            finished = self.Player.skills_manager.execute("Neutral")
-            self.sync()
-            if finished:
-                finished_count += 1
-                if finished_count >= 20:  # 假设需要连续20次完成才算成功
-                    break
-
-        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-            # Perturb waist + lower body only (10:), keep head/arms stable.
-            perturb_action[10:] = np.random.uniform(
-                -self.reset_joint_noise_rad,
-                self.reset_joint_noise_rad,
-                size=(self.no_of_actions - 10,)
-            )
-
-            for _ in range(self.reset_perturb_steps):
-                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-            for i in range(self.reset_recover_steps):
-                # Linearly fade perturbation to help policy start from near-neutral.
-                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-        # memory variables
-        self.sync()  
-        self.initial_position = np.array(self.Player.world.global_position[:2])
-        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions, np.float32)
-        # Randomize global target bearing so policy must learn to rotate toward it first.
-        heading_deg = float(r.global_orientation_euler[2])
-        target_offset = MathOps.rotate_2d_vec(
-            np.array([target_distance, 0.0]),
-            heading_deg + target_bearing_deg,
-            is_rad=False,
-        )
-        point1 = self.initial_position + target_offset
-        self.point_list = [point1]
-        self.target_position = self.point_list[self.waypoint_index]
-        self.initial_height = self.Player.world.global_position[2]
-
-        return self.observe(True), {}
-
-    def render(self, mode='human', close=False):
-        return
-
-    def compute_reward(self, previous_pos, current_pos, action):
-        print(time.time(), self.step_counter)
-        height = float(self.Player.world.global_position[2])
-        robot = self.Player.robot
-
-        joint_pos_rad = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        joint_speed_rad = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-        tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        ang_vel = np.deg2rad(robot.gyroscope)
-        rp_ang_vel_mag = float(np.linalg.norm(ang_vel[:2]))
-
-        # is_fallen = height < 0.55
-        # if is_fallen:
-        #     remain = max(0, 800 - self.step_counter)
-        #     # Strong terminal penalty discourages risky turn-and-fall behaviors.
-        #     return -1
-        
-
-
-        # # 目标方向
-        # to_target = self.target_position - current_pos
-        # dist_to_target = float(np.linalg.norm(to_target))
-        # if dist_to_target < 0.5:
-        #     return 15.0
-
-        # forward_dir = to_target / dist_to_target if dist_to_target > 0.1 else np.array([1.0, 0.0])
-        # delta_pos = current_pos - previous_pos
-        # forward_step = float(np.dot(delta_pos, forward_dir))
-        # lateral_step = float(np.linalg.norm(delta_pos - forward_dir * forward_step))
-
-        # Keep reward simple: turn correctly, stay stable, avoid jerky actions.
-        
-        delta_action_norm = float(np.linalg.norm(action - self.last_action_for_reward))
-        # Cap smoothness penalty so it regularizes behavior without dominating total reward.
-        smoothness_penalty = -min(self.reward_smoothness_cap, self.reward_smoothness_scale * delta_action_norm)
-
-        posture_penalty = -0.45 * tilt_mag
-        # Penalize roll/pitch rotational shake but do not penalize yaw turning directly.
-        ang_vel_penalty = -0.04 * rp_ang_vel_mag
-
-        joint_pos = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        ) * self.train_sim_flip
-
-        left_hip_roll = float(joint_pos[12])
-        right_hip_roll = float(joint_pos[18])
-        left_ankle_roll = float(joint_pos[16])
-        right_ankle_roll = float(joint_pos[22])
-
-        hip_spread = left_hip_roll - right_hip_roll
-        ankle_spread = left_ankle_roll - right_ankle_roll
-        stance_metric = 0.6 * abs(hip_spread) + 0.4 * abs(ankle_spread)
-
-        # Penalize narrow stance (feet too close) and scissoring (cross-leg pattern).
-        stance_collapse_penalty = -4 * max(0.0, self.min_stance_rad - stance_metric)
-        cross_leg_penalty = -2.5 * max(0.0, -(hip_spread * ankle_spread))
-
-
-        # Torso-lower-body linkage: reward coordinated turning, punish waist-only spinning.
-        waist_speed = abs(float(joint_speed_rad[10]))
-        lower_body_speed = float(np.mean(np.abs(joint_speed_rad[11:23])))
-        lower_body_follow_ratio = lower_body_speed / (waist_speed + 1e-4)
-        linkage_reward = 0.24 * min(1.0, lower_body_follow_ratio) * min(1.0, waist_speed / 1.2)
-        waist_only_turn_penalty = -0.20 * max(0.0, waist_speed - 1.35 * lower_body_speed)
-
-        # Extra posture linkage in yaw joints to avoid decoupled torso twist.
-        waist_yaw = abs(float(joint_pos_rad[10]))
-        hip_yaw_mean = 0.5 * (abs(float(joint_pos_rad[13])) + abs(float(joint_pos_rad[19])))
-        yaw_link_reward = 0.12 * math.exp(-abs(waist_yaw - hip_yaw_mean) / 0.22)
-
-        # Turn-to-target shaping.
-        to_target = self.target_position - current_pos
-        dist_to_target = float(np.linalg.norm(to_target))
-        if dist_to_target > 1e-6:
-            target_yaw = math.atan2(float(to_target[1]), float(to_target[0]))
-        else:
-            target_yaw = 0.0
-
-        robot_yaw = math.radians(float(robot.global_orientation_euler[2]))
-        yaw_error = self._wrap_to_pi(target_yaw - robot_yaw)
-
-        # Main heading objective: face the target direction.
-        # heading_align_reward = 1.0 * math.cos(yaw_error)
-
-        abs_yaw_error = abs(yaw_error)
-
-        # Reward reducing heading error between consecutive steps.
-        # Use a deadzone and smaller gain to avoid high-frequency jitter near alignment.
-        if self.last_yaw_error is None:
-            heading_progress_reward = 0.0
-        else:
-            prev_abs_yaw_error = abs(self.last_yaw_error)
-            yaw_err_delta = prev_abs_yaw_error - abs_yaw_error
-            progress_gate = 1.0 if abs_yaw_error > math.radians(4.0) else 0.0
-            heading_progress_reward = 0.70 * progress_gate * yaw_err_delta
-            heading_progress_reward = float(np.clip(heading_progress_reward, -0.70, 0.70))
-        self.last_yaw_error = yaw_error
-
-        yaw_rate = float(np.deg2rad(robot.gyroscope[2]))
-        yaw_rate_abs = abs(yaw_rate)
-        turn_dir = float(np.sign(yaw_error))
-        # Continuous turn shaping prevents reward discontinuity near small heading error.
-        turn_gate = min(1.0, abs_yaw_error / math.radians(45.0))
-        turn_rate_reward = 0.70 * turn_gate * math.tanh(2.0 * turn_dir * yaw_rate)
-        head_toward_bonus = self.reward_head_toward_bonus if abs_yaw_error < math.radians(8.0) else 0.0
-        # After roughly aligning with target, prioritize standing stability over continued aggressive turning.
-        aligned_gate = max(0.0, 1.0 - abs_yaw_error / math.radians(18.0))
-        post_turn_ang_vel_penalty = -0.10 * aligned_gate * min(rp_ang_vel_mag, math.radians(60.0))
-        lower_body_speed_mag = float(np.mean(np.abs(joint_speed_rad[11:23])))
-        post_turn_pose_bonus = 0.30 * aligned_gate * math.exp(-tilt_mag / 0.20) * math.exp(-lower_body_speed_mag / 1.10)
-        # Keep feet separation when aligned so robot does not collapse stance after turning.
-        aligned_stance_bonus = 0.20 * aligned_gate * min(1.0, stance_metric / max(self.min_stance_rad, 1e-4))
-        # Once roughly aligned, damp yaw oscillation and reward keeping a stable stance.
-        anti_oscillation_penalty = -0.08 * min(yaw_rate_abs, math.radians(35.0)) if abs_yaw_error < math.radians(7.0) else 0.0
-        stabilize_bonus = 0.45 if (
-            abs_yaw_error < math.radians(8.0)
-            and yaw_rate_abs < math.radians(10.0)
-            and tilt_mag < 0.28
-        ) else 0.0
-
-        # 改进（线性分段，sigmoid 过渡）
-        if abs_yaw_error < math.radians(15.0):
-            alive_bonus = 2 * (1.0 - abs_yaw_error / math.radians(15.0)) ** 0.5  # 平方根让小角度更敏感
-        else:
-            alive_bonus = max(0.1, 2 * (1.0 - (abs_yaw_error - math.radians(15.0)) / math.radians(75.0)))
-
-        target_height = self.initial_height
-        height_error =  height - target_height
-        # 改进（分段，偏离越多惩罚越重）
-        height_error = height - target_height
-        if abs(height_error) < 0.04:
-            height_penalty = -2.5 * abs(height_error)  # 小偏离，保持线性
-        else:
-            height_penalty = -2.5 * 0.04 - 4.0 * (abs(height_error) - 0.04)  # 大偏离，惩罚加速
-
-        total = (
-            alive_bonus
-            + smoothness_penalty
-            + posture_penalty
-            + ang_vel_penalty
-            + linkage_reward
-            + waist_only_turn_penalty
-            + yaw_link_reward
-            + head_toward_bonus
-            + heading_progress_reward
-            + anti_oscillation_penalty
-            + stabilize_bonus
-            + height_penalty
-            # + post_turn_ang_vel_penalty
-            # + post_turn_pose_bonus
-            # + aligned_stance_bonus
-            # + heading_align_reward
-            + turn_rate_reward
-            # + stance_collapse_penalty
-            # + cross_leg_penalty
-        )
-
-        now = time.time()
-        if self.reward_debug_interval_sec > 0 and now - self._reward_debug_last_time >= self.reward_debug_interval_sec:
-            self._reward_debug_last_time = now
-            self._reward_debug_steps_left = max(1, self.reward_debug_burst_steps)
-
-        if self._reward_debug_steps_left > 0:
-            self._reward_debug_steps_left -= 1
-            # print(
-            #     f"reward_debug: step={self.step_counter}, "
-            #     f"alive_bonus:{alive_bonus:.4f}, "
-            #     # f"heading_align_reward:{heading_align_reward:.4f}, "
-            #     # f"heading_progress_reward:{heading_progress_reward:.4f}, "
-            #     f"head_towards_bonus:{head_toward_bonus},"
-            #     f"posture_penalty:{posture_penalty:.4f}, "
-            #     f"ang_vel_penalty:{ang_vel_penalty:.4f}, "
-            #     f"smoothness_penalty:{smoothness_penalty:.4f}, "
-            #     f"linkage_reward:{linkage_reward:.4f}, "
-            #     f"waist_only_turn_penalty:{waist_only_turn_penalty:.4f}, "
-            #     f"yaw_link_reward:{yaw_link_reward:.4f}, "
-            #     f"anti_oscillation_penalty:{anti_oscillation_penalty:.4f}, "
-            #     f"stabilize_bonus:{stabilize_bonus:.4f}, "
-            #     f"turn_rate_reward:{turn_rate_reward:.4f}, "
-            #     f"total:{total:.4f}"
-            # )
-            
-            self.debug_log(
-                f"reward_debug: step={self.step_counter}, "
-                f"alive_bonus:{alive_bonus:.4f}, "
-                # f"heading_align_reward:{heading_align_reward:.4f}, "
-                f"heading_progress_reward:{heading_progress_reward:.4f}, "
-                f"head_towards_bonus:{head_toward_bonus},"
-                f"posture_penalty:{posture_penalty:.4f}, "
-                f"ang_vel_penalty:{ang_vel_penalty:.4f}, "
-                f"smoothness_penalty:{smoothness_penalty:.4f}, "
-                f"heading_progress_reward:{heading_progress_reward:.4f}, "
-                f"linkage_reward:{linkage_reward:.4f}, "
-                f"waist_only_turn_penalty:{waist_only_turn_penalty:.4f}, "
-                f"yaw_link_reward:{yaw_link_reward:.4f}, "
-                f"anti_oscillation_penalty:{anti_oscillation_penalty:.4f}, "
-                f"stabilize_bonus:{stabilize_bonus:.4f}, "
-                f"height_penalty:{height_penalty:.4f}, "
-                # f"post_turn_ang_vel_penalty:{post_turn_ang_vel_penalty:.4f}, "
-                # f"post_turn_pose_bonus:{post_turn_pose_bonus:.4f}, "
-                f"aligned_stance_bonus:{aligned_stance_bonus:.4f}, "
-                # f"turn_rate_reward:{turn_rate_reward:.4f}, "
-                f"stance_collapse_penalty:{stance_collapse_penalty:.4f}, "
-                f"cross_leg_penalty:{cross_leg_penalty:.4f}, "
-                f"total:{total:.4f}"
-                )
-
-        return total
-
-
-
-    def step(self, action):
-
-        r = self.Player.robot
-        max_action_delta =  0.1# Limit how much the action can change from the previous step to encourage smoother motions.
-        if self.previous_action is not None:
-            action = np.clip(action, self.previous_action - max_action_delta, self.previous_action + max_action_delta)
-        self.previous_action = action.copy()
-
-        self.target_joint_positions = (
-                # self.joint_nominal_position +
-                 self.scaling_factor * action
-        )
-        self.target_joint_positions *= self.train_sim_flip
-
-        for idx, target in enumerate(self.target_joint_positions):
-            r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=40, kd=1.2
-            )
-
-        self.previous_action = action.copy()
-
-        self.sync()  # run simulation step
-        self.step_counter += 1
-
-        if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
-            self.debug_joint_status()
-
-        current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
-
-        # Compute reward based on movement from previous step
-        reward = self.compute_reward(self.previous_pos, current_pos, action)
-
-        # Update previous position
-        self.previous_pos = current_pos.copy()
-        self.last_action_for_reward = action.copy()
-
-        # Fall detection and penalty
-        is_fallen = self.Player.world.global_position[2] < 0.55
-
-        # terminal state: the robot is falling or timeout
-        terminated = is_fallen or self.step_counter > 800 or self.route_completed
-        truncated = False
-
-        return self.observe(), reward, terminated, truncated, {}
-
-
-class Train(Train_Base):
-    def __init__(self, script) -> None:
-        super().__init__(script)
-
-    def train(self, args):
-
-        # --------------------------------------- Learning parameters
-        n_envs = int(os.environ.get("GYM_CPU_N_ENVS", "20"))
-        if n_envs < 1:
-            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
-        server_warmup_sec = float(os.environ.get("GYM_CPU_SERVER_WARMUP_SEC", "3.0"))
-        n_steps_per_env = int(os.environ.get("GYM_CPU_TRAIN_STEPS_PER_ENV", "256"))  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = int(os.environ.get("GYM_CPU_TRAIN_BATCH_SIZE", "512"))  # should be a factor of (n_steps_per_env * n_envs)
-        total_steps = 30000000
-        learning_rate = float(os.environ.get("GYM_CPU_TRAIN_LR", "3e-4"))
-        folder_name = f'Turn_R{self.robot_type}'
-        model_path = f'./scripts/gyms/logs/{folder_name}/'
-
-        print(f"Model path: {model_path}")
-        print(f"Using {n_envs} parallel environments")
-
-        # --------------------------------------- Run algorithm
-        def init_env(i_env, monitor=False):
-            def thunk():
-                env = WalkEnv(self.ip, self.server_p + i_env)
-                if monitor:
-                    env = Monitor(env)
-                return env
-
-            return thunk
-
-        server_log_dir = os.path.join(model_path, "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1, no_render=True, no_realtime=True)  # include 1 extra server for testing
-
-        # Wait for servers to start
-        print(f"Starting {n_envs + 1} rcssservermj servers...")
-        if server_warmup_sec > 0:
-            print(f"Waiting {server_warmup_sec:.1f}s for server warmup...")
-            sleep(server_warmup_sec)
-        print("Servers started, creating environments...")
-
-        env = SubprocVecEnv([init_env(i, monitor=True) for i in range(n_envs)], start_method="spawn")
-        # Use single-process eval env to avoid extra subprocess fragility during callback evaluation.
-        eval_env = DummyVecEnv([init_env(n_envs, monitor=True)])
-
-        try:
-            # Custom policy network architecture
-            policy_kwargs = dict(
-                net_arch=dict(
-                    pi=[512, 256, 128],  # Policy network: 3 layers
-                    vf=[512, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
-            )
-
-            if "model_file" in args:  # retrain
-                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
-                                 batch_size=minibatch_size, learning_rate=learning_rate)
-            else:  # train new model
-                model = PPO(
-                    "MlpPolicy",
-                    env=env,
-                    verbose=1,
-                    n_steps=n_steps_per_env,
-                    batch_size=minibatch_size,
-                    learning_rate=learning_rate,
-                    device="cpu",
-                    policy_kwargs=policy_kwargs,
-                    ent_coef=float(os.environ.get("GYM_CPU_TRAIN_ENT_COEF", "0.05")),  # Entropy coefficient for exploration
-                    clip_range=float(os.environ.get("GYM_CPU_TRAIN_CLIP_RANGE", "0.2")),  # PPO clipping parameter
-                    gae_lambda=0.95,  # GAE lambda
-                    gamma=float(os.environ.get("GYM_CPU_TRAIN_GAMMA", "0.95")), # Discount factor
-                    # target_kl=0.03,
-                    n_epochs=int(os.environ.get("GYM_CPU_TRAIN_EPOCHS", "5")),
-                    tensorboard_log=f"./scripts/gyms/logs/{folder_name}/tensorboard/",
-                    max_grad_norm=float(os.environ.get("GYM_CPU_TRAIN_MAX_GRAD_NORM", "0.5"))
-                )
-
-            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
-                                          eval_freq=n_steps_per_env * 20, save_freq=n_steps_per_env * 20, eval_eps=5,
-                                          backup_env_file=__file__)
-        except KeyboardInterrupt:
-            sleep(1)  # wait for child processes
-            print("\nctrl+c pressed, aborting...\n")
-            servers.kill()
-            return
-
-        env.close()
-        eval_env.close()
-        servers.kill()
-
-    def test(self, args):
-
-        # Uses different server and monitor ports
-        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        test_no_render = os.environ.get("GYM_CPU_TEST_NO_RENDER", "0") == "1"
-        test_no_realtime = os.environ.get("GYM_CPU_TEST_NO_REALTIME", "0") == "1"
-
-        server = Train_Server(
-            self.server_p - 1,
-            self.monitor_p,
-            1,
-            no_render=test_no_render,
-            no_realtime=test_no_realtime,
-        )
-        env = WalkEnv(self.ip, self.server_p - 1)
-        model = PPO.load(args["model_file"], env=env)
-
-        try:
-            self.export_model(args["model_file"], args["model_file"] + ".pkl",
-                              False)  # Export to pkl to create custom behavior
-            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
-        except KeyboardInterrupt:
-            print()
-
-        env.close()
-        server.kill()
-
-
-if __name__ == "__main__":
-    from types import SimpleNamespace
-
-    # 创建默认参数
-    script_args = SimpleNamespace(
-        args=SimpleNamespace(
-            i='127.0.0.1',  # Server IP
-            p=3100,  # Server port
-            m=3200,  # Monitor port
-            r=0,  # Robot type
-            t='Gym',  # Team name
-            u=1  # Uniform number
-        )
-    )
-
-    trainer = Train(script_args)
-
-    run_mode = os.environ.get("GYM_CPU_MODE", "train").strip().lower()
-
-    if run_mode == "test":
-        test_model_file = os.environ.get("GYM_CPU_TEST_MODEL", "scripts/gyms/logs/Turn_R0_004/best_model.zip")
-        test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Turn_R0_004/")
-        trainer.test({"model_file": test_model_file, "folder_dir": test_folder})
-    else:
-        retrain_model = os.environ.get("GYM_CPU_TRAIN_MODEL", "").strip()
-        if retrain_model:
-            trainer.train({"model_file": retrain_model})
-        else:
-            trainer.train({})

scripts/gyms/logs/Turn_R0_001/Walk.py

@@ -1,831 +0,0 @@
-import os
-import numpy as np
-import math
-import time
-from time import sleep
-from random import random
-from random import uniform
-from itertools import count
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.monitor import Monitor
-from stable_baselines3.common.vec_env import SubprocVecEnv, DummyVecEnv
-
-import gymnasium as gym
-from gymnasium import spaces
-
-from scripts.commons.Train_Base import Train_Base
-from scripts.commons.Server import Server as Train_Server
-
-from agent.base_agent import Base_Agent
-from utils.math_ops import MathOps
-
-from scipy.spatial.transform import Rotation as R
-
-'''
-Objective:
-Learn how to run forward using step primitive
-----------
-- class Basic_Run: implements an OpenAI custom gym
-- class Train:  implements algorithms to train a new model or test an existing model
-'''
-
-
-class WalkEnv(gym.Env):
-    def __init__(self, ip, server_p) -> None:
-
-        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
-        self.Player = player = Base_Agent(
-            team_name="Gym",
-            number=1,
-            host=ip,
-            port=server_p
-        )
-        self.robot_type = self.Player.robot
-        self.step_counter = 0  # to limit episode size
-        self.force_play_on = True
-
-        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
-        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
-        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
-        self.isfallen = False
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.debug_every_n_steps = 5
-        self.enable_debug_joint_status = False
-        self.reward_debug_interval_sec = float(os.environ.get("GYM_CPU_REWARD_DEBUG_INTERVAL_SEC", "600"))
-        self.reward_debug_burst_steps = int(os.environ.get("GYM_CPU_REWARD_DEBUG_BURST_STEPS", "10"))
-        self._reward_debug_last_time = time.time()
-        self._reward_debug_steps_left = 0
-        self.calibrate_nominal_from_neutral = True
-        self.auto_calibrate_train_sim_flip = True
-        self.nominal_calibrated_once = False
-        self.flip_calibrated_once = False
-        self._target_hz = 0.0
-        self._target_dt = 0.0
-        self._last_sync_time = None
-        target_hz_env = 0
-        if target_hz_env:
-            try:
-                self._target_hz = float(target_hz_env)
-            except ValueError:
-                self._target_hz = 0.0
-        if self._target_hz > 0.0:
-            self._target_dt = 1.0 / self._target_hz
-
-        # State space
-        # 原始观测大小: 78
-        obs_size = 78
-        self.obs = np.zeros(obs_size, np.float32)
-        self.observation_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(obs_size,),
-            dtype=np.float32
-        )
-
-        action_dim = len(self.Player.robot.ROBOT_MOTORS)
-        self.no_of_actions = action_dim
-        self.action_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(action_dim,),
-            dtype=np.float32
-        )
-
-        # 中立姿态
-        self.joint_nominal_position = np.array(
-            [
-                0.0,
-                0.0,
-                0.0,
-                1.4,
-                0.0,
-                -0.4,
-                0.0,
-                -1.4,
-                0.0,
-                0.4,
-                0.0,
-                -0.4,
-                0.0,
-                0.0,
-                0.8,
-                -0.4,
-                0.0,
-                0.4,
-                0.0,
-                0.0,
-                -0.8,
-                0.4,
-                0.0,
-            ]
-        )
-        self.joint_nominal_position = np.zeros(self.no_of_actions)
-        self.train_sim_flip = np.array(
-            [
-                1.0,  # 0: Head_yaw (he1)
-                -1.0,  # 1: Head_pitch (he2)
-                1.0,  # 2: Left_Shoulder_Pitch (lae1)
-                -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                -1.0,  # 4: Left_Elbow_Pitch (lae3)
-                1.0,  # 5: Left_Elbow_Yaw (lae4)
-                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                -1.0,  # 7: Right_Shoulder_Roll (rae2)
-                1.0,  # 8: Right_Elbow_Pitch (rae3)
-                1.0,  # 9: Right_Elbow_Yaw (rae4)
-                1.0,  # 10: Waist (te1)
-                1.0,  # 11: Left_Hip_Pitch (lle1)
-                -1.0,  # 12: Left_Hip_Roll (lle2)
-                -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,  # 14: Left_Knee_Pitch (lle4)
-                1.0,  # 15: Left_Ankle_Pitch (lle5)
-                -1.0,  # 16: Left_Ankle_Roll (lle6)
-                -1.0,  # 17: Right_Hip_Pitch (rle1)
-                -1.0,  # 18: Right_Hip_Roll (rle2)
-                -1.0,  # 19: Right_Hip_Yaw (rle3)
-                -1.0,  # 20: Right_Knee_Pitch (rle4)
-                -1.0,  # 21: Right_Ankle_Pitch (rle5)
-                -1.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-
-        self.scaling_factor = 0.3
-        # self.scaling_factor = 1
-
-        # Encourage a minimum lateral stance so the policy avoids feet overlap.
-        self.min_stance_rad = 0.10
-
-        # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = False
-        self.reset_beam_yaw_range_deg = float(os.environ.get("GYM_CPU_RESET_BEAM_YAW_RANGE_DEG", "180"))
-        self.reset_target_bearing_range_deg = float(os.environ.get("GYM_CPU_RESET_TARGET_BEARING_RANGE_DEG", "45"))
-        self.reset_target_distance_min = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MIN", "1.2"))
-        self.reset_target_distance_max = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MAX", "2.8"))
-        if self.reset_target_distance_min > self.reset_target_distance_max:
-            self.reset_target_distance_min, self.reset_target_distance_max = (
-                self.reset_target_distance_max,
-                self.reset_target_distance_min,
-            )
-        self.reset_joint_noise_rad = 0.025
-        self.reset_perturb_steps = 4
-        self.reset_recover_steps = 8
-
-        self.reward_smoothness_scale = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_SCALE", "0.06"))
-        self.reward_smoothness_cap = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_CAP", "0.45"))
-        self.reward_head_toward_bonus = float(os.environ.get("GYM_CPU_REWARD_HEAD_TOWARD_BONUS", "0.7"))
-
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Track previous position
-        self.last_yaw_error = None
-        self.Player.server.connect()
-        # sleep(2.0)  # Longer wait for connection to establish completely
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-        self.start_time = time.time()
-
-    def _reconnect_server(self):
-        try:
-            self.Player.server.shutdown()
-        except Exception:
-            pass
-
-        self.Player.server.connect()
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-
-    def _safe_receive_world_update(self, retries=1):
-        last_exc = None
-        for attempt in range(retries + 1):
-            try:
-                self.Player.server.receive()
-                self.Player.world.update()
-                return
-            except (ConnectionResetError, OSError) as exc:
-                last_exc = exc
-                if attempt >= retries:
-                    raise
-                self._reconnect_server()
-        if last_exc is not None:
-            raise last_exc
-
-    def debug_log(self, message):
-        print(message)
-        try:
-            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
-            with open(log_path, "a", encoding="utf-8") as f:
-                f.write(message + "\n")
-        except OSError:
-            pass
-
-    @staticmethod
-    def _wrap_to_pi(angle_rad: float) -> float:
-        return (angle_rad + math.pi) % (2.0 * math.pi) - math.pi
-
-    def observe(self, init=False):
-
-        """获取当前观测值"""
-        robot = self.Player.robot
-        world = self.Player.world
-
-        # Safety check: ensure data is available
-
-        # 计算目标速度
-        raw_target = self.target_position - world.global_position[:2]
-        velocity = MathOps.rotate_2d_vec(
-            raw_target,
-            -robot.global_orientation_euler[2],
-            is_rad=False
-        )
-
-        # 计算相对方向
-        rel_orientation = MathOps.vector_angle(velocity) * 0.3
-        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
-
-        velocity = np.concatenate([velocity, np.array([rel_orientation])])
-        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
-        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
-
-        # 关节状态
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        qpos_qvel_previous_action = np.concatenate([
-            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
-            radian_joint_speeds / 110.0 * self.train_sim_flip,
-            self.previous_action / 10.0,
-        ])
-
-        # 角速度
-        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
-
-        # 投影的重力方向
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-
-        # 组合观测
-        observation = np.concatenate([
-            qpos_qvel_previous_action,
-            ang_vel,
-            velocity,
-            projected_gravity,
-        ])
-
-        observation = np.clip(observation, -10.0, 10.0)
-        return observation.astype(np.float32)
-
-    def sync(self):
-        ''' Run a single simulation step '''
-        self._safe_receive_world_update(retries=1)
-        self.Player.robot.commit_motor_targets_pd()
-        self.Player.server.send()
-        if self._target_dt > 0.0:
-            now = time.time()
-            if self._last_sync_time is None:
-                self._last_sync_time = now
-                return
-            elapsed = now - self._last_sync_time
-            remaining = self._target_dt - elapsed
-            if remaining > 0.0:
-                time.sleep(remaining)
-                now = time.time()
-            self._last_sync_time = now
-
-    def debug_joint_status(self):
-        robot = self.Player.robot
-        actual_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        target_joint_positions = getattr(
-            self,
-            'target_joint_positions',
-            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
-        )
-        joint_error = actual_joint_positions - target_joint_positions
-        leg_slice = slice(11, None)
-
-        self.debug_log(
-            "[WalkDebug] "
-            f"step={self.step_counter} "
-            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
-            f"target_xy={np.round(self.target_position, 3).tolist()} "
-            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
-            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
-            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
-            f"fallen={self.Player.world.global_position[2] < 0.3}"
-        )
-        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
-
-    def reset(self, seed=None, options=None):
-        '''
-        Reset and stabilize the robot
-        Note: for some behaviors it would be better to reduce stabilization or add noise
-        '''
-        r = self.Player.robot
-        super().reset(seed=seed)
-        if seed is not None:
-            np.random.seed(seed)
-
-        target_distance = np.random.uniform(self.reset_target_distance_min, self.reset_target_distance_max)
-        target_bearing_deg = np.random.uniform(-self.reset_target_bearing_range_deg, self.reset_target_bearing_range_deg)
-
-        self.step_counter = 0
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
-        self.last_yaw_error = None
-        self.walk_cycle_step = 0
-        self._reward_debug_steps_left = 0
-
-        # 随机 beam 目标位置和朝向，增加训练多样性
-        beam_x = (random() - 0.5) * 10
-        beam_y = (random() - 0.5) * 10
-        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
-
-        for _ in range(5):
-            self._safe_receive_world_update(retries=2)
-            self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
-            self.Player.server.send()
-
-        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
-        finished_count = 0
-        for _ in range(50):
-            finished = self.Player.skills_manager.execute("Neutral")
-            self.sync()
-            if finished:
-                finished_count += 1
-                if finished_count >= 20:  # 假设需要连续20次完成才算成功
-                    break
-
-        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-            # Perturb waist + lower body only (10:), keep head/arms stable.
-            perturb_action[10:] = np.random.uniform(
-                -self.reset_joint_noise_rad,
-                self.reset_joint_noise_rad,
-                size=(self.no_of_actions - 10,)
-            )
-
-            for _ in range(self.reset_perturb_steps):
-                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-            for i in range(self.reset_recover_steps):
-                # Linearly fade perturbation to help policy start from near-neutral.
-                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-        # memory variables
-        self.sync()  
-        self.initial_position = np.array(self.Player.world.global_position[:2])
-        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions, np.float32)
-        # Randomize global target bearing so policy must learn to rotate toward it first.
-        heading_deg = float(r.global_orientation_euler[2])
-        target_offset = MathOps.rotate_2d_vec(
-            np.array([target_distance, 0.0]),
-            heading_deg + target_bearing_deg,
-            is_rad=False,
-        )
-        point1 = self.initial_position + target_offset
-        self.point_list = [point1]
-        self.target_position = self.point_list[self.waypoint_index]
-        self.initial_height = self.Player.world.global_position[2]
-
-        return self.observe(True), {}
-
-    def render(self, mode='human', close=False):
-        return
-
-    def compute_reward(self, previous_pos, current_pos, action):
-        height = float(self.Player.world.global_position[2])
-        robot = self.Player.robot
-
-        joint_pos_rad = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        joint_speed_rad = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-        tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        ang_vel = np.deg2rad(robot.gyroscope)
-        rp_ang_vel_mag = float(np.linalg.norm(ang_vel[:2]))
-
-        # is_fallen = height < 0.55
-        # if is_fallen:
-        #     remain = max(0, 800 - self.step_counter)
-        #     # Strong terminal penalty discourages risky turn-and-fall behaviors.
-        #     return -1
-        
-
-
-        # # 目标方向
-        # to_target = self.target_position - current_pos
-        # dist_to_target = float(np.linalg.norm(to_target))
-        # if dist_to_target < 0.5:
-        #     return 15.0
-
-        # forward_dir = to_target / dist_to_target if dist_to_target > 0.1 else np.array([1.0, 0.0])
-        # delta_pos = current_pos - previous_pos
-        # forward_step = float(np.dot(delta_pos, forward_dir))
-        # lateral_step = float(np.linalg.norm(delta_pos - forward_dir * forward_step))
-
-        # Keep reward simple: turn correctly, stay stable, avoid jerky actions.
-        
-        delta_action_norm = float(np.linalg.norm(action - self.last_action_for_reward))
-        # Cap smoothness penalty so it regularizes behavior without dominating total reward.
-        smoothness_penalty = -min(self.reward_smoothness_cap, self.reward_smoothness_scale * delta_action_norm)
-
-        posture_penalty = -0.45 * tilt_mag
-        # Penalize roll/pitch rotational shake but do not penalize yaw turning directly.
-        ang_vel_penalty = -0.04 * rp_ang_vel_mag
-
-        joint_pos = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        ) * self.train_sim_flip
-
-        left_hip_roll = float(joint_pos[12])
-        right_hip_roll = float(joint_pos[18])
-        left_ankle_roll = float(joint_pos[16])
-        right_ankle_roll = float(joint_pos[22])
-
-        hip_spread = left_hip_roll - right_hip_roll
-        ankle_spread = left_ankle_roll - right_ankle_roll
-        stance_metric = 0.6 * abs(hip_spread) + 0.4 * abs(ankle_spread)
-
-        # Penalize narrow stance (feet too close) and scissoring (cross-leg pattern).
-        stance_collapse_penalty = -4 * max(0.0, self.min_stance_rad - stance_metric)
-        cross_leg_penalty = -2.5 * max(0.0, -(hip_spread * ankle_spread))
-
-
-        # Torso-lower-body linkage: reward coordinated turning, punish waist-only spinning.
-        waist_speed = abs(float(joint_speed_rad[10]))
-        lower_body_speed = float(np.mean(np.abs(joint_speed_rad[11:23])))
-        lower_body_follow_ratio = lower_body_speed / (waist_speed + 1e-4)
-        linkage_reward = 0.24 * min(1.0, lower_body_follow_ratio) * min(1.0, waist_speed / 1.2)
-        waist_only_turn_penalty = -0.20 * max(0.0, waist_speed - 1.35 * lower_body_speed)
-
-        # Extra posture linkage in yaw joints to avoid decoupled torso twist.
-        waist_yaw = abs(float(joint_pos_rad[10]))
-        hip_yaw_mean = 0.5 * (abs(float(joint_pos_rad[13])) + abs(float(joint_pos_rad[19])))
-        yaw_link_reward = 0.12 * math.exp(-abs(waist_yaw - hip_yaw_mean) / 0.22)
-
-        # Turn-to-target shaping.
-        to_target = self.target_position - current_pos
-        dist_to_target = float(np.linalg.norm(to_target))
-        if dist_to_target > 1e-6:
-            target_yaw = math.atan2(float(to_target[1]), float(to_target[0]))
-        else:
-            target_yaw = 0.0
-
-        robot_yaw = math.radians(float(robot.global_orientation_euler[2]))
-        yaw_error = self._wrap_to_pi(target_yaw - robot_yaw)
-
-        # Main heading objective: face the target direction.
-        # heading_align_reward = 1.0 * math.cos(yaw_error)
-
-        abs_yaw_error = abs(yaw_error)
-
-        # Reward reducing heading error between consecutive steps.
-        # Use a deadzone and smaller gain to avoid high-frequency jitter near alignment.
-        if self.last_yaw_error is None:
-            heading_progress_reward = 0.0
-        else:
-            prev_abs_yaw_error = abs(self.last_yaw_error)
-            yaw_err_delta = prev_abs_yaw_error - abs_yaw_error
-            progress_gate = 1.0 if abs_yaw_error > math.radians(4.0) else 0.0
-            heading_progress_reward = 0.70 * progress_gate * yaw_err_delta
-            heading_progress_reward = float(np.clip(heading_progress_reward, -0.70, 0.70))
-        self.last_yaw_error = yaw_error
-
-        yaw_rate = float(np.deg2rad(robot.gyroscope[2]))
-        yaw_rate_abs = abs(yaw_rate)
-        turn_dir = float(np.sign(yaw_error))
-        # Continuous turn shaping prevents reward discontinuity near small heading error.
-        turn_gate = min(1.0, abs_yaw_error / math.radians(45.0))
-        turn_rate_reward = 0.70 * turn_gate * math.tanh(2.0 * turn_dir * yaw_rate)
-        head_toward_bonus = self.reward_head_toward_bonus if abs_yaw_error < math.radians(8.0) else 0.0
-        # After roughly aligning with target, prioritize standing stability over continued aggressive turning.
-        aligned_gate = max(0.0, 1.0 - abs_yaw_error / math.radians(18.0))
-        post_turn_ang_vel_penalty = -0.10 * aligned_gate * min(rp_ang_vel_mag, math.radians(60.0))
-        lower_body_speed_mag = float(np.mean(np.abs(joint_speed_rad[11:23])))
-        post_turn_pose_bonus = 0.30 * aligned_gate * math.exp(-tilt_mag / 0.20) * math.exp(-lower_body_speed_mag / 1.10)
-        # Keep feet separation when aligned so robot does not collapse stance after turning.
-        aligned_stance_bonus = 0.20 * aligned_gate * min(1.0, stance_metric / max(self.min_stance_rad, 1e-4))
-        # Once roughly aligned, damp yaw oscillation and reward keeping a stable stance.
-        anti_oscillation_penalty = -0.08 * min(yaw_rate_abs, math.radians(35.0)) if abs_yaw_error < math.radians(7.0) else 0.0
-        stabilize_bonus = 0.45 if (
-            abs_yaw_error < math.radians(8.0)
-            and yaw_rate_abs < math.radians(10.0)
-            and tilt_mag < 0.28
-        ) else 0.0
-
-        # 改进（线性分段，sigmoid 过渡）
-        if abs_yaw_error < math.radians(15.0):
-            alive_bonus = 2 * (1.0 - abs_yaw_error / math.radians(15.0)) ** 0.5  # 平方根让小角度更敏感
-        else:
-            alive_bonus = max(0.1, 2 * (1.0 - (abs_yaw_error - math.radians(15.0)) / math.radians(75.0)))
-
-        target_height = self.initial_height
-        height_error =  height - target_height
-        # 改进（分段，偏离越多惩罚越重）
-        height_error = height - target_height
-        if abs(height_error) < 0.04:
-            height_penalty = -2.5 * abs(height_error)  # 小偏离，保持线性
-        else:
-            height_penalty = -2.5 * 0.04 - 4.0 * (abs(height_error) - 0.04)  # 大偏离，惩罚加速
-
-        total = (
-            alive_bonus
-            + smoothness_penalty
-            + posture_penalty
-            + ang_vel_penalty
-            + linkage_reward
-            + waist_only_turn_penalty
-            + yaw_link_reward
-            + head_toward_bonus
-            + heading_progress_reward
-            + anti_oscillation_penalty
-            + stabilize_bonus
-            + height_penalty
-            # + post_turn_ang_vel_penalty
-            # + post_turn_pose_bonus
-            # + aligned_stance_bonus
-            # + heading_align_reward
-            + turn_rate_reward
-            # + stance_collapse_penalty
-            # + cross_leg_penalty
-        )
-
-        now = time.time()
-        if self.reward_debug_interval_sec > 0 and now - self._reward_debug_last_time >= self.reward_debug_interval_sec:
-            self._reward_debug_last_time = now
-            self._reward_debug_steps_left = max(1, self.reward_debug_burst_steps)
-
-        if self._reward_debug_steps_left > 0:
-            self._reward_debug_steps_left -= 1
-            # print(
-            #     f"reward_debug: step={self.step_counter}, "
-            #     f"alive_bonus:{alive_bonus:.4f}, "
-            #     # f"heading_align_reward:{heading_align_reward:.4f}, "
-            #     # f"heading_progress_reward:{heading_progress_reward:.4f}, "
-            #     f"head_towards_bonus:{head_toward_bonus},"
-            #     f"posture_penalty:{posture_penalty:.4f}, "
-            #     f"ang_vel_penalty:{ang_vel_penalty:.4f}, "
-            #     f"smoothness_penalty:{smoothness_penalty:.4f}, "
-            #     f"linkage_reward:{linkage_reward:.4f}, "
-            #     f"waist_only_turn_penalty:{waist_only_turn_penalty:.4f}, "
-            #     f"yaw_link_reward:{yaw_link_reward:.4f}, "
-            #     f"anti_oscillation_penalty:{anti_oscillation_penalty:.4f}, "
-            #     f"stabilize_bonus:{stabilize_bonus:.4f}, "
-            #     f"turn_rate_reward:{turn_rate_reward:.4f}, "
-            #     f"total:{total:.4f}"
-            # )
-            
-            self.debug_log(
-                f"reward_debug: step={self.step_counter}, "
-                f"alive_bonus:{alive_bonus:.4f}, "
-                # f"heading_align_reward:{heading_align_reward:.4f}, "
-                f"heading_progress_reward:{heading_progress_reward:.4f}, "
-                f"head_towards_bonus:{head_toward_bonus},"
-                f"posture_penalty:{posture_penalty:.4f}, "
-                f"ang_vel_penalty:{ang_vel_penalty:.4f}, "
-                f"smoothness_penalty:{smoothness_penalty:.4f}, "
-                f"heading_progress_reward:{heading_progress_reward:.4f}, "
-                f"linkage_reward:{linkage_reward:.4f}, "
-                f"waist_only_turn_penalty:{waist_only_turn_penalty:.4f}, "
-                f"yaw_link_reward:{yaw_link_reward:.4f}, "
-                f"anti_oscillation_penalty:{anti_oscillation_penalty:.4f}, "
-                f"stabilize_bonus:{stabilize_bonus:.4f}, "
-                f"height_penalty:{height_penalty:.4f}, "
-                # f"post_turn_ang_vel_penalty:{post_turn_ang_vel_penalty:.4f}, "
-                # f"post_turn_pose_bonus:{post_turn_pose_bonus:.4f}, "
-                f"aligned_stance_bonus:{aligned_stance_bonus:.4f}, "
-                # f"turn_rate_reward:{turn_rate_reward:.4f}, "
-                f"stance_collapse_penalty:{stance_collapse_penalty:.4f}, "
-                f"cross_leg_penalty:{cross_leg_penalty:.4f}, "
-                f"total:{total:.4f}"
-                )
-
-        return total
-
-
-
-    def step(self, action):
-
-        r = self.Player.robot
-        max_action_delta =  0.1# Limit how much the action can change from the previous step to encourage smoother motions.
-        if self.previous_action is not None:
-            action = np.clip(action, self.previous_action - max_action_delta, self.previous_action + max_action_delta)
-        self.previous_action = action.copy()
-
-        self.target_joint_positions = (
-                # self.joint_nominal_position +
-                 self.scaling_factor * action
-        )
-        self.target_joint_positions *= self.train_sim_flip
-
-        for idx, target in enumerate(self.target_joint_positions):
-            r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=40, kd=1.2
-            )
-
-        self.previous_action = action.copy()
-
-        self.sync()  # run simulation step
-        self.step_counter += 1
-
-        if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
-            self.debug_joint_status()
-
-        current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
-
-        # Compute reward based on movement from previous step
-        reward = self.compute_reward(self.previous_pos, current_pos, action)
-
-        # Update previous position
-        self.previous_pos = current_pos.copy()
-        self.last_action_for_reward = action.copy()
-
-        # Fall detection and penalty
-        is_fallen = self.Player.world.global_position[2] < 0.55
-
-        # terminal state: the robot is falling or timeout
-        terminated = is_fallen or self.step_counter > 800 or self.route_completed
-        truncated = False
-
-        return self.observe(), reward, terminated, truncated, {}
-
-
-class Train(Train_Base):
-    def __init__(self, script) -> None:
-        super().__init__(script)
-
-    def train(self, args):
-
-        # --------------------------------------- Learning parameters
-        n_envs = int(os.environ.get("GYM_CPU_N_ENVS", "20"))
-        if n_envs < 1:
-            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
-        server_warmup_sec = float(os.environ.get("GYM_CPU_SERVER_WARMUP_SEC", "3.0"))
-        n_steps_per_env = int(os.environ.get("GYM_CPU_TRAIN_STEPS_PER_ENV", "256"))  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = int(os.environ.get("GYM_CPU_TRAIN_BATCH_SIZE", "512"))  # should be a factor of (n_steps_per_env * n_envs)
-        total_steps = 30000000
-        learning_rate = float(os.environ.get("GYM_CPU_TRAIN_LR", "3e-4"))
-        folder_name = f'Turn_R{self.robot_type}'
-        model_path = f'./scripts/gyms/logs/{folder_name}/'
-
-        print(f"Model path: {model_path}")
-        print(f"Using {n_envs} parallel environments")
-
-        # --------------------------------------- Run algorithm
-        def init_env(i_env, monitor=False):
-            def thunk():
-                env = WalkEnv(self.ip, self.server_p + i_env)
-                if monitor:
-                    env = Monitor(env)
-                return env
-
-            return thunk
-
-        server_log_dir = os.path.join(model_path, "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1, no_render=True, no_realtime=True)  # include 1 extra server for testing
-
-        # Wait for servers to start
-        print(f"Starting {n_envs + 1} rcssservermj servers...")
-        if server_warmup_sec > 0:
-            print(f"Waiting {server_warmup_sec:.1f}s for server warmup...")
-            sleep(server_warmup_sec)
-        print("Servers started, creating environments...")
-
-        env = SubprocVecEnv([init_env(i, monitor=True) for i in range(n_envs)], start_method="spawn")
-        # Use single-process eval env to avoid extra subprocess fragility during callback evaluation.
-        eval_env = DummyVecEnv([init_env(n_envs, monitor=True)])
-
-        try:
-            # Custom policy network architecture
-            policy_kwargs = dict(
-                net_arch=dict(
-                    pi=[512, 256, 128],  # Policy network: 3 layers
-                    vf=[512, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
-            )
-
-            if "model_file" in args:  # retrain
-                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
-                                 batch_size=minibatch_size, learning_rate=learning_rate)
-            else:  # train new model
-                model = PPO(
-                    "MlpPolicy",
-                    env=env,
-                    verbose=1,
-                    n_steps=n_steps_per_env,
-                    batch_size=minibatch_size,
-                    learning_rate=learning_rate,
-                    device="cpu",
-                    policy_kwargs=policy_kwargs,
-                    ent_coef=float(os.environ.get("GYM_CPU_TRAIN_ENT_COEF", "0.05")),  # Entropy coefficient for exploration
-                    clip_range=float(os.environ.get("GYM_CPU_TRAIN_CLIP_RANGE", "0.2")),  # PPO clipping parameter
-                    gae_lambda=0.95,  # GAE lambda
-                    gamma=float(os.environ.get("GYM_CPU_TRAIN_GAMMA", "0.95")), # Discount factor
-                    # target_kl=0.03,
-                    n_epochs=int(os.environ.get("GYM_CPU_TRAIN_EPOCHS", "5")),
-                    tensorboard_log=f"./scripts/gyms/logs/{folder_name}/tensorboard/",
-                    max_grad_norm=float(os.environ.get("GYM_CPU_TRAIN_MAX_GRAD_NORM", "0.5"))
-                )
-
-            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
-                                          eval_freq=n_steps_per_env * 20, save_freq=n_steps_per_env * 20, eval_eps=5,
-                                          backup_env_file=__file__)
-        except KeyboardInterrupt:
-            sleep(1)  # wait for child processes
-            print("\nctrl+c pressed, aborting...\n")
-            servers.kill()
-            return
-
-        env.close()
-        eval_env.close()
-        servers.kill()
-
-    def test(self, args):
-
-        # Uses different server and monitor ports
-        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        test_no_render = os.environ.get("GYM_CPU_TEST_NO_RENDER", "0") == "1"
-        test_no_realtime = os.environ.get("GYM_CPU_TEST_NO_REALTIME", "0") == "1"
-
-        server = Train_Server(
-            self.server_p - 1,
-            self.monitor_p,
-            1,
-            no_render=test_no_render,
-            no_realtime=test_no_realtime,
-        )
-        env = WalkEnv(self.ip, self.server_p - 1)
-        model = PPO.load(args["model_file"], env=env)
-
-        try:
-            self.export_model(args["model_file"], args["model_file"] + ".pkl",
-                              False)  # Export to pkl to create custom behavior
-            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
-        except KeyboardInterrupt:
-            print()
-
-        env.close()
-        server.kill()
-
-
-if __name__ == "__main__":
-    from types import SimpleNamespace
-
-    # 创建默认参数
-    script_args = SimpleNamespace(
-        args=SimpleNamespace(
-            i='127.0.0.1',  # Server IP
-            p=3100,  # Server port
-            m=3200,  # Monitor port
-            r=0,  # Robot type
-            t='Gym',  # Team name
-            u=1  # Uniform number
-        )
-    )
-
-    trainer = Train(script_args)
-
-    run_mode = os.environ.get("GYM_CPU_MODE", "train").strip().lower()
-
-    if run_mode == "test":
-        test_model_file = os.environ.get("GYM_CPU_TEST_MODEL", "scripts/gyms/logs/Turn_R0_004/best_model.zip")
-        test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Turn_R0_004/")
-        trainer.test({"model_file": test_model_file, "folder_dir": test_folder})
-    else:
-        retrain_model = os.environ.get("GYM_CPU_TRAIN_MODEL", "").strip()
-        if retrain_model:
-            trainer.train({"model_file": retrain_model})
-        else:
-            trainer.train({})

scripts/gyms/logs/Turn_R0_002/Walk.py

@@ -1,831 +0,0 @@
-import os
-import numpy as np
-import math
-import time
-from time import sleep
-from random import random
-from random import uniform
-from itertools import count
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.monitor import Monitor
-from stable_baselines3.common.vec_env import SubprocVecEnv, DummyVecEnv
-
-import gymnasium as gym
-from gymnasium import spaces
-
-from scripts.commons.Train_Base import Train_Base
-from scripts.commons.Server import Server as Train_Server
-
-from agent.base_agent import Base_Agent
-from utils.math_ops import MathOps
-
-from scipy.spatial.transform import Rotation as R
-
-'''
-Objective:
-Learn how to run forward using step primitive
-----------
-- class Basic_Run: implements an OpenAI custom gym
-- class Train:  implements algorithms to train a new model or test an existing model
-'''
-
-
-class WalkEnv(gym.Env):
-    def __init__(self, ip, server_p) -> None:
-
-        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
-        self.Player = player = Base_Agent(
-            team_name="Gym",
-            number=1,
-            host=ip,
-            port=server_p
-        )
-        self.robot_type = self.Player.robot
-        self.step_counter = 0  # to limit episode size
-        self.force_play_on = True
-
-        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
-        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
-        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
-        self.isfallen = False
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.debug_every_n_steps = 5
-        self.enable_debug_joint_status = False
-        self.reward_debug_interval_sec = float(os.environ.get("GYM_CPU_REWARD_DEBUG_INTERVAL_SEC", "600"))
-        self.reward_debug_burst_steps = int(os.environ.get("GYM_CPU_REWARD_DEBUG_BURST_STEPS", "10"))
-        self._reward_debug_last_time = time.time()
-        self._reward_debug_steps_left = 0
-        self.calibrate_nominal_from_neutral = True
-        self.auto_calibrate_train_sim_flip = True
-        self.nominal_calibrated_once = False
-        self.flip_calibrated_once = False
-        self._target_hz = 0.0
-        self._target_dt = 0.0
-        self._last_sync_time = None
-        target_hz_env = 0
-        if target_hz_env:
-            try:
-                self._target_hz = float(target_hz_env)
-            except ValueError:
-                self._target_hz = 0.0
-        if self._target_hz > 0.0:
-            self._target_dt = 1.0 / self._target_hz
-
-        # State space
-        # 原始观测大小: 78
-        obs_size = 78
-        self.obs = np.zeros(obs_size, np.float32)
-        self.observation_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(obs_size,),
-            dtype=np.float32
-        )
-
-        action_dim = len(self.Player.robot.ROBOT_MOTORS)
-        self.no_of_actions = action_dim
-        self.action_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(action_dim,),
-            dtype=np.float32
-        )
-
-        # 中立姿态
-        self.joint_nominal_position = np.array(
-            [
-                0.0,
-                0.0,
-                0.0,
-                1.4,
-                0.0,
-                -0.4,
-                0.0,
-                -1.4,
-                0.0,
-                0.4,
-                0.0,
-                -0.4,
-                0.0,
-                0.0,
-                0.8,
-                -0.4,
-                0.0,
-                0.4,
-                0.0,
-                0.0,
-                -0.8,
-                0.4,
-                0.0,
-            ]
-        )
-        self.joint_nominal_position = np.zeros(self.no_of_actions)
-        self.train_sim_flip = np.array(
-            [
-                1.0,  # 0: Head_yaw (he1)
-                -1.0,  # 1: Head_pitch (he2)
-                1.0,  # 2: Left_Shoulder_Pitch (lae1)
-                -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                -1.0,  # 4: Left_Elbow_Pitch (lae3)
-                1.0,  # 5: Left_Elbow_Yaw (lae4)
-                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                -1.0,  # 7: Right_Shoulder_Roll (rae2)
-                1.0,  # 8: Right_Elbow_Pitch (rae3)
-                1.0,  # 9: Right_Elbow_Yaw (rae4)
-                1.0,  # 10: Waist (te1)
-                1.0,  # 11: Left_Hip_Pitch (lle1)
-                -1.0,  # 12: Left_Hip_Roll (lle2)
-                -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,  # 14: Left_Knee_Pitch (lle4)
-                1.0,  # 15: Left_Ankle_Pitch (lle5)
-                -1.0,  # 16: Left_Ankle_Roll (lle6)
-                -1.0,  # 17: Right_Hip_Pitch (rle1)
-                -1.0,  # 18: Right_Hip_Roll (rle2)
-                -1.0,  # 19: Right_Hip_Yaw (rle3)
-                -1.0,  # 20: Right_Knee_Pitch (rle4)
-                -1.0,  # 21: Right_Ankle_Pitch (rle5)
-                -1.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-
-        self.scaling_factor = 0.3
-        # self.scaling_factor = 1
-
-        # Encourage a minimum lateral stance so the policy avoids feet overlap.
-        self.min_stance_rad = 0.10
-
-        # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = False
-        self.reset_beam_yaw_range_deg = float(os.environ.get("GYM_CPU_RESET_BEAM_YAW_RANGE_DEG", "180"))
-        self.reset_target_bearing_range_deg = float(os.environ.get("GYM_CPU_RESET_TARGET_BEARING_RANGE_DEG", "45"))
-        self.reset_target_distance_min = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MIN", "1.2"))
-        self.reset_target_distance_max = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MAX", "2.8"))
-        if self.reset_target_distance_min > self.reset_target_distance_max:
-            self.reset_target_distance_min, self.reset_target_distance_max = (
-                self.reset_target_distance_max,
-                self.reset_target_distance_min,
-            )
-        self.reset_joint_noise_rad = 0.025
-        self.reset_perturb_steps = 4
-        self.reset_recover_steps = 8
-
-        self.reward_smoothness_scale = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_SCALE", "0.06"))
-        self.reward_smoothness_cap = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_CAP", "0.45"))
-        self.reward_head_toward_bonus = float(os.environ.get("GYM_CPU_REWARD_HEAD_TOWARD_BONUS", "1"))
-
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Track previous position
-        self.last_yaw_error = None
-        self.Player.server.connect()
-        # sleep(2.0)  # Longer wait for connection to establish completely
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-        self.start_time = time.time()
-
-    def _reconnect_server(self):
-        try:
-            self.Player.server.shutdown()
-        except Exception:
-            pass
-
-        self.Player.server.connect()
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-
-    def _safe_receive_world_update(self, retries=1):
-        last_exc = None
-        for attempt in range(retries + 1):
-            try:
-                self.Player.server.receive()
-                self.Player.world.update()
-                return
-            except (ConnectionResetError, OSError) as exc:
-                last_exc = exc
-                if attempt >= retries:
-                    raise
-                self._reconnect_server()
-        if last_exc is not None:
-            raise last_exc
-
-    def debug_log(self, message):
-        print(message)
-        try:
-            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
-            with open(log_path, "a", encoding="utf-8") as f:
-                f.write(message + "\n")
-        except OSError:
-            pass
-
-    @staticmethod
-    def _wrap_to_pi(angle_rad: float) -> float:
-        return (angle_rad + math.pi) % (2.0 * math.pi) - math.pi
-
-    def observe(self, init=False):
-
-        """获取当前观测值"""
-        robot = self.Player.robot
-        world = self.Player.world
-
-        # Safety check: ensure data is available
-
-        # 计算目标速度
-        raw_target = self.target_position - world.global_position[:2]
-        velocity = MathOps.rotate_2d_vec(
-            raw_target,
-            -robot.global_orientation_euler[2],
-            is_rad=False
-        )
-
-        # 计算相对方向
-        rel_orientation = MathOps.vector_angle(velocity) * 0.3
-        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
-
-        velocity = np.concatenate([velocity, np.array([rel_orientation])])
-        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
-        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
-
-        # 关节状态
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        qpos_qvel_previous_action = np.concatenate([
-            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
-            radian_joint_speeds / 110.0 * self.train_sim_flip,
-            self.previous_action / 10.0,
-        ])
-
-        # 角速度
-        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
-
-        # 投影的重力方向
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-
-        # 组合观测
-        observation = np.concatenate([
-            qpos_qvel_previous_action,
-            ang_vel,
-            velocity,
-            projected_gravity,
-        ])
-
-        observation = np.clip(observation, -10.0, 10.0)
-        return observation.astype(np.float32)
-
-    def sync(self):
-        ''' Run a single simulation step '''
-        self._safe_receive_world_update(retries=1)
-        self.Player.robot.commit_motor_targets_pd()
-        self.Player.server.send()
-        if self._target_dt > 0.0:
-            now = time.time()
-            if self._last_sync_time is None:
-                self._last_sync_time = now
-                return
-            elapsed = now - self._last_sync_time
-            remaining = self._target_dt - elapsed
-            if remaining > 0.0:
-                time.sleep(remaining)
-                now = time.time()
-            self._last_sync_time = now
-
-    def debug_joint_status(self):
-        robot = self.Player.robot
-        actual_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        target_joint_positions = getattr(
-            self,
-            'target_joint_positions',
-            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
-        )
-        joint_error = actual_joint_positions - target_joint_positions
-        leg_slice = slice(11, None)
-
-        self.debug_log(
-            "[WalkDebug] "
-            f"step={self.step_counter} "
-            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
-            f"target_xy={np.round(self.target_position, 3).tolist()} "
-            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
-            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
-            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
-            f"fallen={self.Player.world.global_position[2] < 0.3}"
-        )
-        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
-
-    def reset(self, seed=None, options=None):
-        '''
-        Reset and stabilize the robot
-        Note: for some behaviors it would be better to reduce stabilization or add noise
-        '''
-        r = self.Player.robot
-        super().reset(seed=seed)
-        if seed is not None:
-            np.random.seed(seed)
-
-        target_distance = np.random.uniform(self.reset_target_distance_min, self.reset_target_distance_max)
-        target_bearing_deg = np.random.uniform(-self.reset_target_bearing_range_deg, self.reset_target_bearing_range_deg)
-
-        self.step_counter = 0
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
-        self.last_yaw_error = None
-        self.walk_cycle_step = 0
-        self._reward_debug_steps_left = 0
-
-        # 随机 beam 目标位置和朝向，增加训练多样性
-        beam_x = (random() - 0.5) * 10
-        beam_y = (random() - 0.5) * 10
-        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
-
-        for _ in range(5):
-            self._safe_receive_world_update(retries=2)
-            self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
-            self.Player.server.send()
-
-        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
-        finished_count = 0
-        for _ in range(50):
-            finished = self.Player.skills_manager.execute("Neutral")
-            self.sync()
-            if finished:
-                finished_count += 1
-                if finished_count >= 20:  # 假设需要连续20次完成才算成功
-                    break
-
-        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-            # Perturb waist + lower body only (10:), keep head/arms stable.
-            perturb_action[10:] = np.random.uniform(
-                -self.reset_joint_noise_rad,
-                self.reset_joint_noise_rad,
-                size=(self.no_of_actions - 10,)
-            )
-
-            for _ in range(self.reset_perturb_steps):
-                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-            for i in range(self.reset_recover_steps):
-                # Linearly fade perturbation to help policy start from near-neutral.
-                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-        # memory variables
-        self.sync()  
-        self.initial_position = np.array(self.Player.world.global_position[:2])
-        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions, np.float32)
-        # Randomize global target bearing so policy must learn to rotate toward it first.
-        heading_deg = float(r.global_orientation_euler[2])
-        target_offset = MathOps.rotate_2d_vec(
-            np.array([target_distance, 0.0]),
-            heading_deg + target_bearing_deg,
-            is_rad=False,
-        )
-        point1 = self.initial_position + target_offset
-        self.point_list = [point1]
-        self.target_position = self.point_list[self.waypoint_index]
-        self.initial_height = self.Player.world.global_position[2]
-
-        return self.observe(True), {}
-
-    def render(self, mode='human', close=False):
-        return
-
-    def compute_reward(self, previous_pos, current_pos, action):
-        height = float(self.Player.world.global_position[2])
-        robot = self.Player.robot
-
-        joint_pos_rad = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        joint_speed_rad = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-        tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        ang_vel = np.deg2rad(robot.gyroscope)
-        rp_ang_vel_mag = float(np.linalg.norm(ang_vel[:2]))
-
-        # is_fallen = height < 0.55
-        # if is_fallen:
-        #     remain = max(0, 800 - self.step_counter)
-        #     # Strong terminal penalty discourages risky turn-and-fall behaviors.
-        #     return -1
-        
-
-
-        # # 目标方向
-        # to_target = self.target_position - current_pos
-        # dist_to_target = float(np.linalg.norm(to_target))
-        # if dist_to_target < 0.5:
-        #     return 15.0
-
-        # forward_dir = to_target / dist_to_target if dist_to_target > 0.1 else np.array([1.0, 0.0])
-        # delta_pos = current_pos - previous_pos
-        # forward_step = float(np.dot(delta_pos, forward_dir))
-        # lateral_step = float(np.linalg.norm(delta_pos - forward_dir * forward_step))
-
-        # Keep reward simple: turn correctly, stay stable, avoid jerky actions.
-        
-        delta_action_norm = float(np.linalg.norm(action - self.last_action_for_reward))
-        # Cap smoothness penalty so it regularizes behavior without dominating total reward.
-        smoothness_penalty = -min(self.reward_smoothness_cap, self.reward_smoothness_scale * delta_action_norm)
-
-        posture_penalty = -0.45 * tilt_mag
-        # Penalize roll/pitch rotational shake but do not penalize yaw turning directly.
-        ang_vel_penalty = -0.04 * rp_ang_vel_mag
-
-        joint_pos = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        ) * self.train_sim_flip
-
-        left_hip_roll = float(joint_pos[12])
-        right_hip_roll = float(joint_pos[18])
-        left_ankle_roll = float(joint_pos[16])
-        right_ankle_roll = float(joint_pos[22])
-
-        hip_spread = left_hip_roll - right_hip_roll
-        ankle_spread = left_ankle_roll - right_ankle_roll
-        stance_metric = 0.5 * abs(hip_spread) + 0.5 * abs(ankle_spread)
-
-        # Penalize narrow stance (feet too close) and scissoring (cross-leg pattern).
-        stance_collapse_penalty = -3 * max(0.0, self.min_stance_rad - stance_metric)
-        cross_leg_penalty = -2.5 * max(0.0, -(hip_spread * ankle_spread))
-
-
-        # Torso-lower-body linkage: reward coordinated turning, punish waist-only spinning.
-        waist_speed = abs(float(joint_speed_rad[10]))
-        lower_body_speed = float(np.mean(np.abs(joint_speed_rad[11:23])))
-        lower_body_follow_ratio = lower_body_speed / (waist_speed + 1e-4)
-        linkage_reward = 0.24 * min(1.0, lower_body_follow_ratio) * min(1.0, waist_speed / 1.2)
-        waist_only_turn_penalty = -0.20 * max(0.0, waist_speed - 1.35 * lower_body_speed)
-
-        # Extra posture linkage in yaw joints to avoid decoupled torso twist.
-        waist_yaw = abs(float(joint_pos_rad[10]))
-        hip_yaw_mean = 0.5 * (abs(float(joint_pos_rad[13])) + abs(float(joint_pos_rad[19])))
-        yaw_link_reward = 0.12 * math.exp(-abs(waist_yaw - hip_yaw_mean) / 0.22)
-
-        # Turn-to-target shaping.
-        to_target = self.target_position - current_pos
-        dist_to_target = float(np.linalg.norm(to_target))
-        if dist_to_target > 1e-6:
-            target_yaw = math.atan2(float(to_target[1]), float(to_target[0]))
-        else:
-            target_yaw = 0.0
-
-        robot_yaw = math.radians(float(robot.global_orientation_euler[2]))
-        yaw_error = self._wrap_to_pi(target_yaw - robot_yaw)
-
-        # Main heading objective: face the target direction.
-        # heading_align_reward = 1.0 * math.cos(yaw_error)
-
-        abs_yaw_error = abs(yaw_error)
-
-        # Reward reducing heading error between consecutive steps.
-        # Use a deadzone and smaller gain to avoid high-frequency jitter near alignment.
-        if self.last_yaw_error is None:
-            heading_progress_reward = 0.0
-        else:
-            prev_abs_yaw_error = abs(self.last_yaw_error)
-            yaw_err_delta = prev_abs_yaw_error - abs_yaw_error
-            progress_gate = 1.0 if abs_yaw_error > math.radians(4.0) else 0.0
-            heading_progress_reward = progress_gate * yaw_err_delta
-            heading_progress_reward = float(np.clip(heading_progress_reward, 1, 1))
-        self.last_yaw_error = yaw_error
-
-        yaw_rate = float(np.deg2rad(robot.gyroscope[2]))
-        yaw_rate_abs = abs(yaw_rate)
-        turn_dir = float(np.sign(yaw_error))
-        # Continuous turn shaping prevents reward discontinuity near small heading error.
-        turn_gate = min(1.0, abs_yaw_error / math.radians(45.0))
-        turn_rate_reward = 0.70 * turn_gate * math.tanh(2.0 * turn_dir * yaw_rate)
-        head_toward_bonus = self.reward_head_toward_bonus if abs_yaw_error < math.radians(8.0) else 0.0
-        # After roughly aligning with target, prioritize standing stability over continued aggressive turning.
-        aligned_gate = max(0.0, 1.0 - abs_yaw_error / math.radians(18.0))
-        post_turn_ang_vel_penalty = -0.10 * aligned_gate * min(rp_ang_vel_mag, math.radians(60.0))
-        lower_body_speed_mag = float(np.mean(np.abs(joint_speed_rad[11:23])))
-        post_turn_pose_bonus = 0.30 * aligned_gate * math.exp(-tilt_mag / 0.20) * math.exp(-lower_body_speed_mag / 1.10)
-        # Keep feet separation when aligned so robot does not collapse stance after turning.
-        aligned_stance_bonus = 0.20 * aligned_gate * min(1.0, stance_metric / max(self.min_stance_rad, 1e-4))
-        # Once roughly aligned, damp yaw oscillation and reward keeping a stable stance.
-        anti_oscillation_penalty = -0.08 * min(yaw_rate_abs, math.radians(35.0)) if abs_yaw_error < math.radians(7.0) else 0.0
-        stabilize_bonus = 0.6 if (
-            abs_yaw_error < math.radians(8.0)
-            and yaw_rate_abs < math.radians(10.0)
-            and tilt_mag < 0.28
-        ) else 0.0
-
-        # 改进（线性分段，sigmoid 过渡）
-        if abs_yaw_error < math.radians(15.0):
-            alive_bonus = 2 * (1.0 - abs_yaw_error / math.radians(15.0)) ** 0.5  # 平方根让小角度更敏感
-        else:
-            alive_bonus = max(0.1, 2 * (1.0 - (abs_yaw_error - math.radians(15.0)) / math.radians(75.0)))
-
-        target_height = self.initial_height
-        height_error =  height - target_height
-        # 改进（分段，偏离越多惩罚越重）
-        height_error = height - target_height
-        if abs(height_error) < 0.04:
-            height_penalty = -2.5 * abs(height_error)  # 小偏离，保持线性
-        else:
-            height_penalty = -2.5 * 0.04 - 4.0 * (abs(height_error) - 0.04)  # 大偏离，惩罚加速
-
-        total = (
-            alive_bonus
-            + smoothness_penalty
-            + posture_penalty
-            + ang_vel_penalty
-            + linkage_reward
-            + waist_only_turn_penalty
-            + yaw_link_reward
-            + head_toward_bonus
-            + heading_progress_reward
-            + anti_oscillation_penalty
-            + stabilize_bonus
-            + height_penalty
-            # + post_turn_ang_vel_penalty
-            # + post_turn_pose_bonus
-            # + aligned_stance_bonus
-            # + heading_align_reward
-            + turn_rate_reward
-            + stance_collapse_penalty
-            + cross_leg_penalty
-        )
-
-        now = time.time()
-        if self.reward_debug_interval_sec > 0 and now - self._reward_debug_last_time >= self.reward_debug_interval_sec:
-            self._reward_debug_last_time = now
-            self._reward_debug_steps_left = max(1, self.reward_debug_burst_steps)
-
-        if self._reward_debug_steps_left > 0:
-            self._reward_debug_steps_left -= 1
-            # print(
-            #     f"reward_debug: step={self.step_counter}, "
-            #     f"alive_bonus:{alive_bonus:.4f}, "
-            #     # f"heading_align_reward:{heading_align_reward:.4f}, "
-            #     # f"heading_progress_reward:{heading_progress_reward:.4f}, "
-            #     f"head_towards_bonus:{head_toward_bonus},"
-            #     f"posture_penalty:{posture_penalty:.4f}, "
-            #     f"ang_vel_penalty:{ang_vel_penalty:.4f}, "
-            #     f"smoothness_penalty:{smoothness_penalty:.4f}, "
-            #     f"linkage_reward:{linkage_reward:.4f}, "
-            #     f"waist_only_turn_penalty:{waist_only_turn_penalty:.4f}, "
-            #     f"yaw_link_reward:{yaw_link_reward:.4f}, "
-            #     f"anti_oscillation_penalty:{anti_oscillation_penalty:.4f}, "
-            #     f"stabilize_bonus:{stabilize_bonus:.4f}, "
-            #     f"turn_rate_reward:{turn_rate_reward:.4f}, "
-            #     f"total:{total:.4f}"
-            # )
-            
-            self.debug_log(
-                f"reward_debug: step={self.step_counter}, "
-                f"alive_bonus:{alive_bonus:.4f}, "
-                # f"heading_align_reward:{heading_align_reward:.4f}, "
-                f"heading_progress_reward:{heading_progress_reward:.4f}, "
-                f"head_towards_bonus:{head_toward_bonus},"
-                f"posture_penalty:{posture_penalty:.4f}, "
-                f"ang_vel_penalty:{ang_vel_penalty:.4f}, "
-                f"smoothness_penalty:{smoothness_penalty:.4f}, "
-                f"heading_progress_reward:{heading_progress_reward:.4f}, "
-                f"linkage_reward:{linkage_reward:.4f}, "
-                f"waist_only_turn_penalty:{waist_only_turn_penalty:.4f}, "
-                f"yaw_link_reward:{yaw_link_reward:.4f}, "
-                f"anti_oscillation_penalty:{anti_oscillation_penalty:.4f}, "
-                f"stabilize_bonus:{stabilize_bonus:.4f}, "
-                f"height_penalty:{height_penalty:.4f}, "
-                # f"post_turn_ang_vel_penalty:{post_turn_ang_vel_penalty:.4f}, "
-                # f"post_turn_pose_bonus:{post_turn_pose_bonus:.4f}, "
-                f"aligned_stance_bonus:{aligned_stance_bonus:.4f}, "
-                # f"turn_rate_reward:{turn_rate_reward:.4f}, "
-                f"stance_collapse_penalty:{stance_collapse_penalty:.4f}, "
-                f"cross_leg_penalty:{cross_leg_penalty:.4f}, "
-                f"total:{total:.4f}"
-                )
-
-        return total
-
-
-
-    def step(self, action):
-
-        r = self.Player.robot
-        max_action_delta =  0.1# Limit how much the action can change from the previous step to encourage smoother motions.
-        if self.previous_action is not None:
-            action = np.clip(action, self.previous_action - max_action_delta, self.previous_action + max_action_delta)
-        self.previous_action = action.copy()
-
-        self.target_joint_positions = (
-                # self.joint_nominal_position +
-                 self.scaling_factor * action
-        )
-        self.target_joint_positions *= self.train_sim_flip
-
-        for idx, target in enumerate(self.target_joint_positions):
-            r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=40, kd=1.2
-            )
-
-        self.previous_action = action.copy()
-
-        self.sync()  # run simulation step
-        self.step_counter += 1
-
-        if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
-            self.debug_joint_status()
-
-        current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
-
-        # Compute reward based on movement from previous step
-        reward = self.compute_reward(self.previous_pos, current_pos, action)
-
-        # Update previous position
-        self.previous_pos = current_pos.copy()
-        self.last_action_for_reward = action.copy()
-
-        # Fall detection and penalty
-        is_fallen = self.Player.world.global_position[2] < 0.55
-
-        # terminal state: the robot is falling or timeout
-        terminated = is_fallen or self.step_counter > 800 or self.route_completed
-        truncated = False
-
-        return self.observe(), reward, terminated, truncated, {}
-
-
-class Train(Train_Base):
-    def __init__(self, script) -> None:
-        super().__init__(script)
-
-    def train(self, args):
-
-        # --------------------------------------- Learning parameters
-        n_envs = int(os.environ.get("GYM_CPU_N_ENVS", "20"))
-        if n_envs < 1:
-            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
-        server_warmup_sec = float(os.environ.get("GYM_CPU_SERVER_WARMUP_SEC", "3.0"))
-        n_steps_per_env = int(os.environ.get("GYM_CPU_TRAIN_STEPS_PER_ENV", "256"))  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = int(os.environ.get("GYM_CPU_TRAIN_BATCH_SIZE", "512"))  # should be a factor of (n_steps_per_env * n_envs)
-        total_steps = 30000000
-        learning_rate = float(os.environ.get("GYM_CPU_TRAIN_LR", "3e-4"))
-        folder_name = f'Turn_R{self.robot_type}'
-        model_path = f'./scripts/gyms/logs/{folder_name}/'
-
-        print(f"Model path: {model_path}")
-        print(f"Using {n_envs} parallel environments")
-
-        # --------------------------------------- Run algorithm
-        def init_env(i_env, monitor=False):
-            def thunk():
-                env = WalkEnv(self.ip, self.server_p + i_env)
-                if monitor:
-                    env = Monitor(env)
-                return env
-
-            return thunk
-
-        server_log_dir = os.path.join(model_path, "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1, no_render=True, no_realtime=True)  # include 1 extra server for testing
-
-        # Wait for servers to start
-        print(f"Starting {n_envs + 1} rcssservermj servers...")
-        if server_warmup_sec > 0:
-            print(f"Waiting {server_warmup_sec:.1f}s for server warmup...")
-            sleep(server_warmup_sec)
-        print("Servers started, creating environments...")
-
-        env = SubprocVecEnv([init_env(i, monitor=True) for i in range(n_envs)], start_method="spawn")
-        # Use single-process eval env to avoid extra subprocess fragility during callback evaluation.
-        eval_env = DummyVecEnv([init_env(n_envs, monitor=True)])
-
-        try:
-            # Custom policy network architecture
-            policy_kwargs = dict(
-                net_arch=dict(
-                    pi=[512, 256, 128],  # Policy network: 3 layers
-                    vf=[512, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
-            )
-
-            if "model_file" in args:  # retrain
-                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
-                                 batch_size=minibatch_size, learning_rate=learning_rate)
-            else:  # train new model
-                model = PPO(
-                    "MlpPolicy",
-                    env=env,
-                    verbose=1,
-                    n_steps=n_steps_per_env,
-                    batch_size=minibatch_size,
-                    learning_rate=learning_rate,
-                    device="cpu",
-                    policy_kwargs=policy_kwargs,
-                    ent_coef=float(os.environ.get("GYM_CPU_TRAIN_ENT_COEF", "0.05")),  # Entropy coefficient for exploration
-                    clip_range=float(os.environ.get("GYM_CPU_TRAIN_CLIP_RANGE", "0.2")),  # PPO clipping parameter
-                    gae_lambda=0.95,  # GAE lambda
-                    gamma=float(os.environ.get("GYM_CPU_TRAIN_GAMMA", "0.95")), # Discount factor
-                    # target_kl=0.03,
-                    n_epochs=int(os.environ.get("GYM_CPU_TRAIN_EPOCHS", "5")),
-                    tensorboard_log=f"./scripts/gyms/logs/{folder_name}/tensorboard/",
-                    max_grad_norm=float(os.environ.get("GYM_CPU_TRAIN_MAX_GRAD_NORM", "0.5"))
-                )
-
-            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
-                                          eval_freq=n_steps_per_env * 20, save_freq=n_steps_per_env * 20, eval_eps=7,
-                                          backup_env_file=__file__)
-        except KeyboardInterrupt:
-            sleep(1)  # wait for child processes
-            print("\nctrl+c pressed, aborting...\n")
-            servers.kill()
-            return
-
-        env.close()
-        eval_env.close()
-        servers.kill()
-
-    def test(self, args):
-
-        # Uses different server and monitor ports
-        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        test_no_render = os.environ.get("GYM_CPU_TEST_NO_RENDER", "0") == "1"
-        test_no_realtime = os.environ.get("GYM_CPU_TEST_NO_REALTIME", "0") == "1"
-
-        server = Train_Server(
-            self.server_p - 1,
-            self.monitor_p,
-            1,
-            no_render=test_no_render,
-            no_realtime=test_no_realtime,
-        )
-        env = WalkEnv(self.ip, self.server_p - 1)
-        model = PPO.load(args["model_file"], env=env)
-
-        try:
-            self.export_model(args["model_file"], args["model_file"] + ".pkl",
-                              False)  # Export to pkl to create custom behavior
-            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
-        except KeyboardInterrupt:
-            print()
-
-        env.close()
-        server.kill()
-
-
-if __name__ == "__main__":
-    from types import SimpleNamespace
-
-    # 创建默认参数
-    script_args = SimpleNamespace(
-        args=SimpleNamespace(
-            i='127.0.0.1',  # Server IP
-            p=3100,  # Server port
-            m=3200,  # Monitor port
-            r=0,  # Robot type
-            t='Gym',  # Team name
-            u=1  # Uniform number
-        )
-    )
-
-    trainer = Train(script_args)
-
-    run_mode = os.environ.get("GYM_CPU_MODE", "train").strip().lower()
-
-    if run_mode == "test":
-        test_model_file = os.environ.get("GYM_CPU_TEST_MODEL", "scripts/gyms/logs/Turn_R0_004/best_model.zip")
-        test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Turn_R0_004/")
-        trainer.test({"model_file": test_model_file, "folder_dir": test_folder})
-    else:
-        retrain_model = os.environ.get("GYM_CPU_TRAIN_MODEL", "").strip()
-        if retrain_model:
-            trainer.train({"model_file": retrain_model})
-        else:
-            trainer.train({})

scripts/gyms/logs/Turn_R0_003/Walk.py

@@ -1,755 +0,0 @@
-import os
-import numpy as np
-import math
-import time
-from time import sleep
-from random import random
-from random import uniform
-from itertools import count
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.monitor import Monitor
-from stable_baselines3.common.vec_env import SubprocVecEnv, DummyVecEnv
-
-import gymnasium as gym
-from gymnasium import spaces
-
-from scripts.commons.Train_Base import Train_Base
-from scripts.commons.Server import Server as Train_Server
-
-from agent.base_agent import Base_Agent
-from utils.math_ops import MathOps
-
-from scipy.spatial.transform import Rotation as R
-
-'''
-Objective:
-Learn how to run forward using step primitive
-----------
-- class Basic_Run: implements an OpenAI custom gym
-- class Train:  implements algorithms to train a new model or test an existing model
-'''
-
-
-class WalkEnv(gym.Env):
-    def __init__(self, ip, server_p) -> None:
-
-        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
-        self.Player = player = Base_Agent(
-            team_name="Gym",
-            number=1,
-            host=ip,
-            port=server_p
-        )
-        self.robot_type = self.Player.robot
-        self.step_counter = 0  # to limit episode size
-        self.force_play_on = True
-
-        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
-        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
-        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
-        self.isfallen = False
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.debug_every_n_steps = 5
-        self.enable_debug_joint_status = False
-        self.reward_debug_interval_sec = float(os.environ.get("GYM_CPU_REWARD_DEBUG_INTERVAL_SEC", "600"))
-        self.reward_debug_burst_steps = int(os.environ.get("GYM_CPU_REWARD_DEBUG_BURST_STEPS", "10"))
-        self._reward_debug_last_time = time.time()
-        self._reward_debug_steps_left = 0
-        self.calibrate_nominal_from_neutral = True
-        self.auto_calibrate_train_sim_flip = True
-        self.nominal_calibrated_once = False
-        self.flip_calibrated_once = False
-        self._target_hz = 0.0
-        self._target_dt = 0.0
-        self._last_sync_time = None
-        target_hz_env = 0
-        if target_hz_env:
-            try:
-                self._target_hz = float(target_hz_env)
-            except ValueError:
-                self._target_hz = 0.0
-        if self._target_hz > 0.0:
-            self._target_dt = 1.0 / self._target_hz
-
-        # State space
-        # 原始观测大小: 78
-        obs_size = 78
-        self.obs = np.zeros(obs_size, np.float32)
-        self.observation_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(obs_size,),
-            dtype=np.float32
-        )
-
-        action_dim = len(self.Player.robot.ROBOT_MOTORS)
-        self.no_of_actions = action_dim
-        self.action_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(action_dim,),
-            dtype=np.float32
-        )
-
-        # 中立姿态
-        self.joint_nominal_position = np.array(
-            [
-                0.0,
-                0.0,
-                0.0,
-                1.4,
-                0.0,
-                -0.4,
-                0.0,
-                -1.4,
-                0.0,
-                0.4,
-                0.0,
-                -0.4,
-                0.0,
-                0.0,
-                0.8,
-                -0.4,
-                0.0,
-                0.4,
-                0.0,
-                0.0,
-                -0.8,
-                0.4,
-                0.0,
-            ]
-        )
-        self.joint_nominal_position = np.zeros(self.no_of_actions)
-        self.train_sim_flip = np.array(
-            [
-                1.0,  # 0: Head_yaw (he1)
-                -1.0,  # 1: Head_pitch (he2)
-                1.0,  # 2: Left_Shoulder_Pitch (lae1)
-                -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                -1.0,  # 4: Left_Elbow_Pitch (lae3)
-                1.0,  # 5: Left_Elbow_Yaw (lae4)
-                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                -1.0,  # 7: Right_Shoulder_Roll (rae2)
-                1.0,  # 8: Right_Elbow_Pitch (rae3)
-                1.0,  # 9: Right_Elbow_Yaw (rae4)
-                1.0,  # 10: Waist (te1)
-                1.0,  # 11: Left_Hip_Pitch (lle1)
-                -1.0,  # 12: Left_Hip_Roll (lle2)
-                -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,  # 14: Left_Knee_Pitch (lle4)
-                1.0,  # 15: Left_Ankle_Pitch (lle5)
-                -1.0,  # 16: Left_Ankle_Roll (lle6)
-                -1.0,  # 17: Right_Hip_Pitch (rle1)
-                -1.0,  # 18: Right_Hip_Roll (rle2)
-                -1.0,  # 19: Right_Hip_Yaw (rle3)
-                -1.0,  # 20: Right_Knee_Pitch (rle4)
-                -1.0,  # 21: Right_Ankle_Pitch (rle5)
-                -1.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-
-        self.scaling_factor = 0.3
-        # self.scaling_factor = 1
-
-        # Encourage a minimum lateral stance so the policy avoids feet overlap.
-        self.min_stance_rad = 0.10
-
-        # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = False
-        self.reset_beam_yaw_range_deg = float(os.environ.get("GYM_CPU_RESET_BEAM_YAW_RANGE_DEG", "180"))
-        self.reset_target_bearing_range_deg = float(os.environ.get("GYM_CPU_RESET_TARGET_BEARING_RANGE_DEG", "45"))
-        self.reset_target_distance_min = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MIN", "1.2"))
-        self.reset_target_distance_max = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MAX", "2.8"))
-        if self.reset_target_distance_min > self.reset_target_distance_max:
-            self.reset_target_distance_min, self.reset_target_distance_max = (
-                self.reset_target_distance_max,
-                self.reset_target_distance_min,
-            )
-        self.reset_joint_noise_rad = 0.025
-        self.reset_perturb_steps = 4
-        self.reset_recover_steps = 8
-
-        self.reward_smoothness_scale = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_SCALE", "0.06"))
-        self.reward_smoothness_cap = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_CAP", "0.45"))
-        self.reward_head_toward_bonus = float(os.environ.get("GYM_CPU_REWARD_HEAD_TOWARD_BONUS", "1"))
-
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Track previous position
-        self.last_yaw_error = None
-        self.Player.server.connect()
-        # sleep(2.0)  # Longer wait for connection to establish completely
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-        self.start_time = time.time()
-
-    def _reconnect_server(self):
-        try:
-            self.Player.server.shutdown()
-        except Exception:
-            pass
-
-        self.Player.server.connect()
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-
-    def _safe_receive_world_update(self, retries=1):
-        last_exc = None
-        for attempt in range(retries + 1):
-            try:
-                self.Player.server.receive()
-                self.Player.world.update()
-                return
-            except (ConnectionResetError, OSError) as exc:
-                last_exc = exc
-                if attempt >= retries:
-                    raise
-                self._reconnect_server()
-        if last_exc is not None:
-            raise last_exc
-
-    def debug_log(self, message):
-        print(message)
-        try:
-            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
-            with open(log_path, "a", encoding="utf-8") as f:
-                f.write(message + "\n")
-        except OSError:
-            pass
-
-    @staticmethod
-    def _wrap_to_pi(angle_rad: float) -> float:
-        return (angle_rad + math.pi) % (2.0 * math.pi) - math.pi
-
-    def observe(self, init=False):
-
-        """获取当前观测值"""
-        robot = self.Player.robot
-        world = self.Player.world
-
-        # Safety check: ensure data is available
-
-        # 计算目标速度
-        raw_target = self.target_position - world.global_position[:2]
-        velocity = MathOps.rotate_2d_vec(
-            raw_target,
-            -robot.global_orientation_euler[2],
-            is_rad=False
-        )
-
-        # 计算相对方向
-        rel_orientation = MathOps.vector_angle(velocity) * 0.3
-        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
-
-        velocity = np.concatenate([velocity, np.array([rel_orientation])])
-        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
-        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
-
-        # 关节状态
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        qpos_qvel_previous_action = np.concatenate([
-            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
-            radian_joint_speeds / 110.0 * self.train_sim_flip,
-            self.previous_action / 10.0,
-        ])
-
-        # 角速度
-        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
-
-        # 投影的重力方向
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-
-        # 组合观测
-        observation = np.concatenate([
-            qpos_qvel_previous_action,
-            ang_vel,
-            velocity,
-            projected_gravity,
-        ])
-
-        observation = np.clip(observation, -10.0, 10.0)
-        return observation.astype(np.float32)
-
-    def sync(self):
-        ''' Run a single simulation step '''
-        self._safe_receive_world_update(retries=1)
-        self.Player.robot.commit_motor_targets_pd()
-        self.Player.server.send()
-        if self._target_dt > 0.0:
-            now = time.time()
-            if self._last_sync_time is None:
-                self._last_sync_time = now
-                return
-            elapsed = now - self._last_sync_time
-            remaining = self._target_dt - elapsed
-            if remaining > 0.0:
-                time.sleep(remaining)
-                now = time.time()
-            self._last_sync_time = now
-
-    def debug_joint_status(self):
-        robot = self.Player.robot
-        actual_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        target_joint_positions = getattr(
-            self,
-            'target_joint_positions',
-            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
-        )
-        joint_error = actual_joint_positions - target_joint_positions
-        leg_slice = slice(11, None)
-
-        self.debug_log(
-            "[WalkDebug] "
-            f"step={self.step_counter} "
-            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
-            f"target_xy={np.round(self.target_position, 3).tolist()} "
-            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
-            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
-            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
-            f"fallen={self.Player.world.global_position[2] < 0.3}"
-        )
-        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
-
-    def reset(self, seed=None, options=None):
-        '''
-        Reset and stabilize the robot
-        Note: for some behaviors it would be better to reduce stabilization or add noise
-        '''
-        r = self.Player.robot
-        super().reset(seed=seed)
-        if seed is not None:
-            np.random.seed(seed)
-
-        target_distance = np.random.uniform(self.reset_target_distance_min, self.reset_target_distance_max)
-        target_bearing_deg = np.random.uniform(-self.reset_target_bearing_range_deg, self.reset_target_bearing_range_deg)
-
-        self.step_counter = 0
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
-        self.last_yaw_error = None
-        self.walk_cycle_step = 0
-        self._reward_debug_steps_left = 0
-
-        # 随机 beam 目标位置和朝向，增加训练多样性
-        beam_x = (random() - 0.5) * 10
-        beam_y = (random() - 0.5) * 10
-        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
-
-        for _ in range(5):
-            self._safe_receive_world_update(retries=2)
-            self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
-            self.Player.server.send()
-
-        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
-        finished_count = 0
-        for _ in range(50):
-            finished = self.Player.skills_manager.execute("Neutral")
-            self.sync()
-            if finished:
-                finished_count += 1
-                if finished_count >= 20:  # 假设需要连续20次完成才算成功
-                    break
-
-        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-            # Perturb waist + lower body only (10:), keep head/arms stable.
-            perturb_action[10:] = np.random.uniform(
-                -self.reset_joint_noise_rad,
-                self.reset_joint_noise_rad,
-                size=(self.no_of_actions - 10,)
-            )
-
-            for _ in range(self.reset_perturb_steps):
-                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-            for i in range(self.reset_recover_steps):
-                # Linearly fade perturbation to help policy start from near-neutral.
-                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-        # memory variables
-        self.sync()  
-        self.initial_position = np.array(self.Player.world.global_position[:2])
-        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions, np.float32)
-        # Randomize global target bearing so policy must learn to rotate toward it first.
-        heading_deg = float(r.global_orientation_euler[2])
-        target_offset = MathOps.rotate_2d_vec(
-            np.array([target_distance, 0.0]),
-            heading_deg + target_bearing_deg,
-            is_rad=False,
-        )
-        point1 = self.initial_position + target_offset
-        self.point_list = [point1]
-        self.target_position = self.point_list[self.waypoint_index]
-        self.initial_height = self.Player.world.global_position[2]
-
-        return self.observe(True), {}
-
-    def render(self, mode='human', close=False):
-        return
-
-    
-    def compute_reward(self, previous_pos, current_pos, action):
-        height = float(self.Player.world.global_position[2])
-        robot = self.Player.robot
-
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-        tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        ang_vel = np.deg2rad(robot.gyroscope)
-        rp_ang_vel_mag = float(np.linalg.norm(ang_vel[:2]))
-
-        is_fallen = height < 0.55
-        if is_fallen:
-            # remain = max(0, 800 - self.step_counter)
-            # return -8.0 - 0.01 * remain
-            return -1.0
-        
-
-
-
-        # Turn-to-target shaping.
-        to_target = self.target_position - current_pos
-        dist_to_target = float(np.linalg.norm(to_target))
-        if dist_to_target > 1e-6:
-            target_yaw = math.atan2(float(to_target[1]), float(to_target[0]))
-        else:
-            target_yaw = 0.0
-
-        robot_yaw = math.radians(float(robot.global_orientation_euler[2]))
-        yaw_error = target_yaw - robot_yaw
-
-        # Main heading objective: face the target direction.
-        # heading_align_reward = 1.0 * math.cos(yaw_error)
-
-        abs_yaw_error = abs(yaw_error)
-        alive_bonus = 2.0 * max(0.0, 1.0 - abs_yaw_error / math.pi)
-
-        # action_penalty = -0.01 * float(np.linalg.norm(action))
-        smoothness_penalty = -0.01 * float(np.linalg.norm(action - self.last_action_for_reward))
-
-        posture_penalty = -0.45 * tilt_mag
-        # Penalize roll/pitch rotational shake but do not penalize yaw turning directly.
-        ang_vel_penalty = -0.04 * rp_ang_vel_mag
-
-        joint_pos = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        ) * self.train_sim_flip
-        left_hip_roll = float(joint_pos[12])
-        right_hip_roll = float(joint_pos[18])
-        left_ankle_roll = float(joint_pos[16])
-        right_ankle_roll = float(joint_pos[22])
-
-        hip_spread = left_hip_roll - right_hip_roll
-        ankle_spread = left_ankle_roll - right_ankle_roll
-        stance_metric = 0.6 * abs(hip_spread) + 0.4 * abs(ankle_spread)
-
-        # Penalize narrow stance (feet too close) and scissoring (cross-leg pattern).
-        stance_collapse_penalty = -4.0 * max(0.0, self.min_stance_rad - stance_metric)
-        cross_leg_penalty = -1.2 * max(0.0, -(hip_spread * ankle_spread))
-
-        target_height = self.initial_height
-        height_error =  height - target_height
-        height_penalty = -0.5 * abs(height_error)  # 惩罚高度偏离，系数可调
-
-        # # 在 compute_reward 开头附近，添加高度变化率计算
-        # if not hasattr(self, 'last_height'):
-        #     self.last_height = height
-        #     self.last_height_time = self.step_counter  # 可选，用于时间间隔
-        # height_rate = height - self.last_height  # 正为上升，负为下降
-        # self.last_height = height
-
-        # 惩罚高度下降（负变化率）
-        # height_down_penalty = -5.0 * max(0, -height_rate)  # 系数可调，-height_rate 为正表示下降幅度
-
-        # # 在 compute_reward 中
-        # if self.step_counter > 50:
-        #     avg_prev_action = np.mean(self.prev_action_history, axis=0)
-        #     novelty = float(np.linalg.norm(action - avg_prev_action))
-        #     exploration_bonus = 0.05 * novelty
-        # else:
-        #     exploration_bonus = 0
-
-        # self.prev_action_history[self.history_idx] = action
-        # self.history_idx = (self.history_idx + 1) % 50
-
-
-        total = (
-            # progress_reward  + 
-                 alive_bonus + 
-                 # lateral_penalty +
-                 # action_penalty +
-                smoothness_penalty +
-                 posture_penalty
-                 + ang_vel_penalty
-                 + height_penalty
-                 + stance_collapse_penalty
-                 + cross_leg_penalty
-                #  + exploration_bonus
-                # + height_down_penalty
-                 )
-        
-        now = time.time()
-        if self.reward_debug_interval_sec > 0 and now - self._reward_debug_last_time >= self.reward_debug_interval_sec:
-            self._reward_debug_last_time = now
-            self._reward_debug_steps_left = max(1, self.reward_debug_burst_steps)
-
-        if self._reward_debug_steps_left > 0:
-            self._reward_debug_steps_left -= 1
-            self.debug_log(
-                f"height_penalty:{height_penalty:.4f}",
-                f"smoothness_penalty:{smoothness_penalty:.4f},",
-                f"posture_penalty:{posture_penalty:.4f}",
-                f"stance_collapse_penalty:{stance_collapse_penalty:.4f}",
-                f"cross_leg_penalty:{cross_leg_penalty:.4f}",
-                f"ang_vel_penalty:{ang_vel_penalty:.4f}",
-                #   f"height_down_penalty:{height_down_penalty:.4f}",
-                #   f"exploration_bonus:{exploration_bonus:.4f}"
-                f"alive_bonus:{alive_bonus:.4f},"
-                f"abs_yaw_error:{abs_yaw_error:.4f}"
-                f"total:{total:.4f}"
-                )
-        if time.time() - self.start_time >= 600:
-            self.start_time = time.time()
-            self.debug_log(
-                #   f"progress_reward:{progress_reward:.4f}",
-                #   f"lateral_penalty:{lateral_penalty:.4f}",
-                #   f"action_penalty:{action_penalty:.4f}"s, 
-                  f"height_penalty:{height_penalty:.4f}",
-                  f"smoothness_penalty:{smoothness_penalty:.4f},",
-                  f"posture_penalty:{posture_penalty:.4f}",
-                  f"stance_collapse_penalty:{stance_collapse_penalty:.4f}",
-                  f"cross_leg_penalty:{cross_leg_penalty:.4f}",
-                #   f"ang_vel_penalty:{ang_vel_penalty:.4f}",
-                #   f"height_down_penalty:{height_down_penalty:.4f}",
-                #   f"exploration_bonus:{exploration_bonus:.4f}"
-            )
-
-        return total
-
-
-
-    def step(self, action):
-
-        r = self.Player.robot
-        max_action_delta =  0.1# Limit how much the action can change from the previous step to encourage smoother motions.
-        if self.previous_action is not None:
-            action = np.clip(action, self.previous_action - max_action_delta, self.previous_action + max_action_delta)
-        self.previous_action = action.copy()
-
-        self.target_joint_positions = (
-                # self.joint_nominal_position +
-                 self.scaling_factor * action
-        )
-        self.target_joint_positions *= self.train_sim_flip
-
-        for idx, target in enumerate(self.target_joint_positions):
-            r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=40, kd=1.2
-            )
-
-        self.previous_action = action.copy()
-
-        self.sync()  # run simulation step
-        self.step_counter += 1
-
-        if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
-            self.debug_joint_status()
-
-        current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
-
-        # Compute reward based on movement from previous step
-        reward = self.compute_reward(self.previous_pos, current_pos, action)
-
-        # Update previous position
-        self.previous_pos = current_pos.copy()
-        self.last_action_for_reward = action.copy()
-
-        # Fall detection and penalty
-        is_fallen = self.Player.world.global_position[2] < 0.55
-
-        # terminal state: the robot is falling or timeout
-        terminated = is_fallen or self.step_counter > 800 or self.route_completed
-        truncated = False
-
-        return self.observe(), reward, terminated, truncated, {}
-
-
-class Train(Train_Base):
-    def __init__(self, script) -> None:
-        super().__init__(script)
-
-    def train(self, args):
-
-        # --------------------------------------- Learning parameters
-        n_envs = int(os.environ.get("GYM_CPU_N_ENVS", "20"))
-        if n_envs < 1:
-            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
-        server_warmup_sec = float(os.environ.get("GYM_CPU_SERVER_WARMUP_SEC", "3.0"))
-        n_steps_per_env = int(os.environ.get("GYM_CPU_TRAIN_STEPS_PER_ENV", "512"))  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = int(os.environ.get("GYM_CPU_TRAIN_BATCH_SIZE", "512"))  # should be a factor of (n_steps_per_env * n_envs)
-        total_steps = 30000000
-        learning_rate = float(os.environ.get("GYM_CPU_TRAIN_LR", "3e-4"))
-        folder_name = f'Turn_R{self.robot_type}'
-        model_path = f'./scripts/gyms/logs/{folder_name}/'
-
-        print(f"Model path: {model_path}")
-        print(f"Using {n_envs} parallel environments")
-
-        # --------------------------------------- Run algorithm
-        def init_env(i_env, monitor=False):
-            def thunk():
-                env = WalkEnv(self.ip, self.server_p + i_env)
-                if monitor:
-                    env = Monitor(env)
-                return env
-
-            return thunk
-
-        server_log_dir = os.path.join(model_path, "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1, no_render=True, no_realtime=True)  # include 1 extra server for testing
-
-        # Wait for servers to start
-        print(f"Starting {n_envs + 1} rcssservermj servers...")
-        if server_warmup_sec > 0:
-            print(f"Waiting {server_warmup_sec:.1f}s for server warmup...")
-            sleep(server_warmup_sec)
-        print("Servers started, creating environments...")
-
-        env = SubprocVecEnv([init_env(i, monitor=True) for i in range(n_envs)], start_method="spawn")
-        # Use single-process eval env to avoid extra subprocess fragility during callback evaluation.
-        eval_env = DummyVecEnv([init_env(n_envs, monitor=True)])
-
-        try:
-            # Custom policy network architecture
-            policy_kwargs = dict(
-                net_arch=dict(
-                    pi=[512, 256, 128],  # Policy network: 3 layers
-                    vf=[512, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
-            )
-
-            if "model_file" in args:  # retrain
-                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
-                                 batch_size=minibatch_size, learning_rate=learning_rate)
-            else:  # train new model
-                model = PPO(
-                    "MlpPolicy",
-                    env=env,
-                    verbose=1,
-                    n_steps=n_steps_per_env,
-                    batch_size=minibatch_size,
-                    learning_rate=learning_rate,
-                    device="cpu",
-                    policy_kwargs=policy_kwargs,
-                    ent_coef=float(os.environ.get("GYM_CPU_TRAIN_ENT_COEF", "0.05")),  # Entropy coefficient for exploration
-                    clip_range=float(os.environ.get("GYM_CPU_TRAIN_CLIP_RANGE", "0.2")),  # PPO clipping parameter
-                    gae_lambda=0.95,  # GAE lambda
-                    gamma=float(os.environ.get("GYM_CPU_TRAIN_GAMMA", "0.95")), # Discount factor
-                    # target_kl=0.03,
-                    n_epochs=int(os.environ.get("GYM_CPU_TRAIN_EPOCHS", "5")),
-                    tensorboard_log=f"./scripts/gyms/logs/{folder_name}/tensorboard/",
-                    max_grad_norm=float(os.environ.get("GYM_CPU_TRAIN_MAX_GRAD_NORM", "0.5"))
-                )
-
-            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
-                                          eval_freq=n_steps_per_env * 20, save_freq=n_steps_per_env * 20, eval_eps=7,
-                                          backup_env_file=__file__)
-        except KeyboardInterrupt:
-            sleep(1)  # wait for child processes
-            print("\nctrl+c pressed, aborting...\n")
-            servers.kill()
-            return
-
-        env.close()
-        eval_env.close()
-        servers.kill()
-
-    def test(self, args):
-
-        # Uses different server and monitor ports
-        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        test_no_render = os.environ.get("GYM_CPU_TEST_NO_RENDER", "0") == "1"
-        test_no_realtime = os.environ.get("GYM_CPU_TEST_NO_REALTIME", "0") == "1"
-
-        server = Train_Server(
-            self.server_p - 1,
-            self.monitor_p,
-            1,
-            no_render=test_no_render,
-            no_realtime=test_no_realtime,
-        )
-        env = WalkEnv(self.ip, self.server_p - 1)
-        model = PPO.load(args["model_file"], env=env)
-
-        try:
-            self.export_model(args["model_file"], args["model_file"] + ".pkl",
-                              False)  # Export to pkl to create custom behavior
-            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
-        except KeyboardInterrupt:
-            print()
-
-        env.close()
-        server.kill()
-
-
-if __name__ == "__main__":
-    from types import SimpleNamespace
-
-    # 创建默认参数
-    script_args = SimpleNamespace(
-        args=SimpleNamespace(
-            i='127.0.0.1',  # Server IP
-            p=3100,  # Server port
-            m=3200,  # Monitor port
-            r=0,  # Robot type
-            t='Gym',  # Team name
-            u=1  # Uniform number
-        )
-    )
-
-    trainer = Train(script_args)
-
-    run_mode = os.environ.get("GYM_CPU_MODE", "train").strip().lower()
-
-    if run_mode == "test":
-        test_model_file = os.environ.get("GYM_CPU_TEST_MODEL", "scripts/gyms/logs/Turn_R0_004/best_model.zip")
-        test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Turn_R0_004/")
-        trainer.test({"model_file": test_model_file, "folder_dir": test_folder})
-    else:
-        retrain_model = os.environ.get("GYM_CPU_TRAIN_MODEL", "").strip()
-        if retrain_model:
-            trainer.train({"model_file": retrain_model})
-        else:
-            trainer.train({})

scripts/gyms/logs/Turn_R0_004/Walk.py

@@ -1,754 +0,0 @@
-import os
-import numpy as np
-import math
-import time
-from time import sleep
-from random import random
-from random import uniform
-from itertools import count
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.monitor import Monitor
-from stable_baselines3.common.vec_env import SubprocVecEnv, DummyVecEnv
-
-import gymnasium as gym
-from gymnasium import spaces
-
-from scripts.commons.Train_Base import Train_Base
-from scripts.commons.Server import Server as Train_Server
-
-from agent.base_agent import Base_Agent
-from utils.math_ops import MathOps
-
-from scipy.spatial.transform import Rotation as R
-
-'''
-Objective:
-Learn how to run forward using step primitive
-----------
-- class Basic_Run: implements an OpenAI custom gym
-- class Train:  implements algorithms to train a new model or test an existing model
-'''
-
-
-class WalkEnv(gym.Env):
-    def __init__(self, ip, server_p) -> None:
-
-        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
-        self.Player = player = Base_Agent(
-            team_name="Gym",
-            number=1,
-            host=ip,
-            port=server_p
-        )
-        self.robot_type = self.Player.robot
-        self.step_counter = 0  # to limit episode size
-        self.force_play_on = True
-
-        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
-        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
-        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
-        self.isfallen = False
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.debug_every_n_steps = 5
-        self.enable_debug_joint_status = False
-        self.reward_debug_interval_sec = float(os.environ.get("GYM_CPU_REWARD_DEBUG_INTERVAL_SEC", "600"))
-        self.reward_debug_burst_steps = int(os.environ.get("GYM_CPU_REWARD_DEBUG_BURST_STEPS", "10"))
-        self._reward_debug_last_time = time.time()
-        self._reward_debug_steps_left = 0
-        self.calibrate_nominal_from_neutral = True
-        self.auto_calibrate_train_sim_flip = True
-        self.nominal_calibrated_once = False
-        self.flip_calibrated_once = False
-        self._target_hz = 0.0
-        self._target_dt = 0.0
-        self._last_sync_time = None
-        target_hz_env = 0
-        if target_hz_env:
-            try:
-                self._target_hz = float(target_hz_env)
-            except ValueError:
-                self._target_hz = 0.0
-        if self._target_hz > 0.0:
-            self._target_dt = 1.0 / self._target_hz
-
-        # State space
-        # 原始观测大小: 78
-        obs_size = 78
-        self.obs = np.zeros(obs_size, np.float32)
-        self.observation_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(obs_size,),
-            dtype=np.float32
-        )
-
-        action_dim = len(self.Player.robot.ROBOT_MOTORS)
-        self.no_of_actions = action_dim
-        self.action_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(action_dim,),
-            dtype=np.float32
-        )
-
-        # 中立姿态
-        self.joint_nominal_position = np.array(
-            [
-                0.0,
-                0.0,
-                0.0,
-                1.4,
-                0.0,
-                -0.4,
-                0.0,
-                -1.4,
-                0.0,
-                0.4,
-                0.0,
-                -0.4,
-                0.0,
-                0.0,
-                0.8,
-                -0.4,
-                0.0,
-                0.4,
-                0.0,
-                0.0,
-                -0.8,
-                0.4,
-                0.0,
-            ]
-        )
-        self.joint_nominal_position = np.zeros(self.no_of_actions)
-        self.train_sim_flip = np.array(
-            [
-                1.0,  # 0: Head_yaw (he1)
-                -1.0,  # 1: Head_pitch (he2)
-                1.0,  # 2: Left_Shoulder_Pitch (lae1)
-                -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                -1.0,  # 4: Left_Elbow_Pitch (lae3)
-                1.0,  # 5: Left_Elbow_Yaw (lae4)
-                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                -1.0,  # 7: Right_Shoulder_Roll (rae2)
-                1.0,  # 8: Right_Elbow_Pitch (rae3)
-                1.0,  # 9: Right_Elbow_Yaw (rae4)
-                1.0,  # 10: Waist (te1)
-                1.0,  # 11: Left_Hip_Pitch (lle1)
-                -1.0,  # 12: Left_Hip_Roll (lle2)
-                -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,  # 14: Left_Knee_Pitch (lle4)
-                1.0,  # 15: Left_Ankle_Pitch (lle5)
-                -1.0,  # 16: Left_Ankle_Roll (lle6)
-                -1.0,  # 17: Right_Hip_Pitch (rle1)
-                -1.0,  # 18: Right_Hip_Roll (rle2)
-                -1.0,  # 19: Right_Hip_Yaw (rle3)
-                -1.0,  # 20: Right_Knee_Pitch (rle4)
-                -1.0,  # 21: Right_Ankle_Pitch (rle5)
-                -1.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-
-        self.scaling_factor = 0.3
-        # self.scaling_factor = 1
-
-        # Encourage a minimum lateral stance so the policy avoids feet overlap.
-        self.min_stance_rad = 0.10
-
-        # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = False
-        self.reset_beam_yaw_range_deg = float(os.environ.get("GYM_CPU_RESET_BEAM_YAW_RANGE_DEG", "180"))
-        self.reset_target_bearing_range_deg = float(os.environ.get("GYM_CPU_RESET_TARGET_BEARING_RANGE_DEG", "45"))
-        self.reset_target_distance_min = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MIN", "1.2"))
-        self.reset_target_distance_max = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MAX", "2.8"))
-        if self.reset_target_distance_min > self.reset_target_distance_max:
-            self.reset_target_distance_min, self.reset_target_distance_max = (
-                self.reset_target_distance_max,
-                self.reset_target_distance_min,
-            )
-        self.reset_joint_noise_rad = 0.025
-        self.reset_perturb_steps = 4
-        self.reset_recover_steps = 8
-
-        self.reward_smoothness_scale = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_SCALE", "0.06"))
-        self.reward_smoothness_cap = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_CAP", "0.45"))
-        self.reward_head_toward_bonus = float(os.environ.get("GYM_CPU_REWARD_HEAD_TOWARD_BONUS", "1"))
-
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Track previous position
-        self.last_yaw_error = None
-        self.Player.server.connect()
-        # sleep(2.0)  # Longer wait for connection to establish completely
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-        self.start_time = time.time()
-
-    def _reconnect_server(self):
-        try:
-            self.Player.server.shutdown()
-        except Exception:
-            pass
-
-        self.Player.server.connect()
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-
-    def _safe_receive_world_update(self, retries=1):
-        last_exc = None
-        for attempt in range(retries + 1):
-            try:
-                self.Player.server.receive()
-                self.Player.world.update()
-                return
-            except (ConnectionResetError, OSError) as exc:
-                last_exc = exc
-                if attempt >= retries:
-                    raise
-                self._reconnect_server()
-        if last_exc is not None:
-            raise last_exc
-
-    def debug_log(self, message):
-        print(message)
-        try:
-            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
-            with open(log_path, "a", encoding="utf-8") as f:
-                f.write(message + "\n")
-        except OSError:
-            pass
-
-    @staticmethod
-    def _wrap_to_pi(angle_rad: float) -> float:
-        return (angle_rad + math.pi) % (2.0 * math.pi) - math.pi
-
-    def observe(self, init=False):
-
-        """获取当前观测值"""
-        robot = self.Player.robot
-        world = self.Player.world
-
-        # Safety check: ensure data is available
-
-        # 计算目标速度
-        raw_target = self.target_position - world.global_position[:2]
-        velocity = MathOps.rotate_2d_vec(
-            raw_target,
-            -robot.global_orientation_euler[2],
-            is_rad=False
-        )
-
-        # 计算相对方向
-        rel_orientation = MathOps.vector_angle(velocity) * 0.3
-        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
-
-        velocity = np.concatenate([velocity, np.array([rel_orientation])])
-        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
-        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
-
-        # 关节状态
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        qpos_qvel_previous_action = np.concatenate([
-            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
-            radian_joint_speeds / 110.0 * self.train_sim_flip,
-            self.previous_action / 10.0,
-        ])
-
-        # 角速度
-        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
-
-        # 投影的重力方向
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-
-        # 组合观测
-        observation = np.concatenate([
-            qpos_qvel_previous_action,
-            ang_vel,
-            velocity,
-            projected_gravity,
-        ])
-
-        observation = np.clip(observation, -10.0, 10.0)
-        return observation.astype(np.float32)
-
-    def sync(self):
-        ''' Run a single simulation step '''
-        self._safe_receive_world_update(retries=1)
-        self.Player.robot.commit_motor_targets_pd()
-        self.Player.server.send()
-        if self._target_dt > 0.0:
-            now = time.time()
-            if self._last_sync_time is None:
-                self._last_sync_time = now
-                return
-            elapsed = now - self._last_sync_time
-            remaining = self._target_dt - elapsed
-            if remaining > 0.0:
-                time.sleep(remaining)
-                now = time.time()
-            self._last_sync_time = now
-
-    def debug_joint_status(self):
-        robot = self.Player.robot
-        actual_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        target_joint_positions = getattr(
-            self,
-            'target_joint_positions',
-            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
-        )
-        joint_error = actual_joint_positions - target_joint_positions
-        leg_slice = slice(11, None)
-
-        self.debug_log(
-            "[WalkDebug] "
-            f"step={self.step_counter} "
-            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
-            f"target_xy={np.round(self.target_position, 3).tolist()} "
-            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
-            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
-            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
-            f"fallen={self.Player.world.global_position[2] < 0.3}"
-        )
-        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
-
-    def reset(self, seed=None, options=None):
-        '''
-        Reset and stabilize the robot
-        Note: for some behaviors it would be better to reduce stabilization or add noise
-        '''
-        r = self.Player.robot
-        super().reset(seed=seed)
-        if seed is not None:
-            np.random.seed(seed)
-
-        target_distance = np.random.uniform(self.reset_target_distance_min, self.reset_target_distance_max)
-        target_bearing_deg = np.random.uniform(-self.reset_target_bearing_range_deg, self.reset_target_bearing_range_deg)
-
-        self.step_counter = 0
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
-        self.last_yaw_error = None
-        self.walk_cycle_step = 0
-        self._reward_debug_steps_left = 0
-
-        # 随机 beam 目标位置和朝向，增加训练多样性
-        beam_x = (random() - 0.5) * 10
-        beam_y = (random() - 0.5) * 10
-        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
-
-        for _ in range(5):
-            self._safe_receive_world_update(retries=2)
-            self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
-            self.Player.server.send()
-
-        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
-        finished_count = 0
-        for _ in range(50):
-            finished = self.Player.skills_manager.execute("Neutral")
-            self.sync()
-            if finished:
-                finished_count += 1
-                if finished_count >= 20:  # 假设需要连续20次完成才算成功
-                    break
-
-        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-            # Perturb waist + lower body only (10:), keep head/arms stable.
-            perturb_action[10:] = np.random.uniform(
-                -self.reset_joint_noise_rad,
-                self.reset_joint_noise_rad,
-                size=(self.no_of_actions - 10,)
-            )
-
-            for _ in range(self.reset_perturb_steps):
-                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-            for i in range(self.reset_recover_steps):
-                # Linearly fade perturbation to help policy start from near-neutral.
-                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-        # memory variables
-        self.sync()  
-        self.initial_position = np.array(self.Player.world.global_position[:2])
-        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions, np.float32)
-        # Randomize global target bearing so policy must learn to rotate toward it first.
-        heading_deg = float(r.global_orientation_euler[2])
-        target_offset = MathOps.rotate_2d_vec(
-            np.array([target_distance, 0.0]),
-            heading_deg + target_bearing_deg,
-            is_rad=False,
-        )
-        point1 = self.initial_position + target_offset
-        self.point_list = [point1]
-        self.target_position = self.point_list[self.waypoint_index]
-        self.initial_height = self.Player.world.global_position[2]
-
-        return self.observe(True), {}
-
-    def render(self, mode='human', close=False):
-        return
-
-    
-    def compute_reward(self, previous_pos, current_pos, action):
-        height = float(self.Player.world.global_position[2])
-        robot = self.Player.robot
-
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-        tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        ang_vel = np.deg2rad(robot.gyroscope)
-        rp_ang_vel_mag = float(np.linalg.norm(ang_vel[:2]))
-
-        is_fallen = height < 0.55
-        if is_fallen:
-            # remain = max(0, 800 - self.step_counter)
-            # return -8.0 - 0.01 * remain
-            return -1.0
-        
-
-
-
-        # Turn-to-target shaping.
-        to_target = self.target_position - current_pos
-        dist_to_target = float(np.linalg.norm(to_target))
-        if dist_to_target > 1e-6:
-            target_yaw = math.atan2(float(to_target[1]), float(to_target[0]))
-        else:
-            target_yaw = 0.0
-
-        robot_yaw = math.radians(float(robot.global_orientation_euler[2]))
-        yaw_error = target_yaw - robot_yaw
-
-        # Main heading objective: face the target direction.
-        # heading_align_reward = 1.0 * math.cos(yaw_error)
-
-        abs_yaw_error = abs(yaw_error)
-        alive_bonus = 2.0 * max(0.0, 1.0 - abs_yaw_error / math.pi)
-
-        if self.last_yaw_error is None:
-            heading_progress_reward = 0.0
-        else:
-            prev_abs_yaw_error = abs(self.last_yaw_error)
-            yaw_err_delta = prev_abs_yaw_error - abs_yaw_error
-            progress_gate = 1.0 if abs_yaw_error > math.radians(4.0) else 0.0
-            heading_progress_reward =  progress_gate * yaw_err_delta
-            heading_progress_reward = float(np.clip(heading_progress_reward, -0.7, 0.7))
-        self.last_yaw_error = yaw_error
-
-        # action_penalty = -0.01 * float(np.linalg.norm(action))
-        smoothness_penalty = -0.02 * float(np.linalg.norm(action - self.last_action_for_reward))
-
-        posture_penalty = -0.6 * tilt_mag
-        # Penalize roll/pitch rotational shake but do not penalize yaw turning directly.
-        ang_vel_penalty = -0.06 * rp_ang_vel_mag
-
-        joint_pos = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        ) * self.train_sim_flip
-        left_hip_roll = float(joint_pos[12])
-        right_hip_roll = float(joint_pos[18])
-        left_ankle_roll = float(joint_pos[16])
-        right_ankle_roll = float(joint_pos[22])
-
-        hip_spread = left_hip_roll - right_hip_roll if right_hip_roll > 0.03 and left_hip_roll > 0.03 else 0.0
-        ankle_spread = left_ankle_roll - right_ankle_roll if right_ankle_roll > 0.03 and left_ankle_roll > 0.03 else 0.0
-        stance_metric = 0.6 * abs(hip_spread) + 0.4 * abs(ankle_spread)
-
-        # Penalize narrow stance (feet too close) and scissoring (cross-leg pattern).
-        stance_collapse_penalty = -4.0 * max(0.0, self.min_stance_rad - stance_metric) 
-        cross_leg_penalty = -1.2 * max(0.0, -(hip_spread * ankle_spread))
-
-        target_height = self.initial_height
-        height_error =  height - target_height
-        height_error = height - target_height
-        
-        height_penalty = -math.exp(15*abs(height_error))
-
-        # # 在 compute_reward 开头附近，添加高度变化率计算
-        # if not hasattr(self, 'last_height'):
-        #     self.last_height = height
-        #     self.last_height_time = self.step_counter  # 可选，用于时间间隔
-        # height_rate = height - self.last_height  # 正为上升，负为下降
-        # self.last_height = height
-
-        # 惩罚高度下降（负变化率）
-        # height_down_penalty = -5.0 * max(0, -height_rate)  # 系数可调，-height_rate 为正表示下降幅度
-
-        # # 在 compute_reward 中
-        # if self.step_counter > 50:
-        #     avg_prev_action = np.mean(self.prev_action_history, axis=0)
-        #     novelty = float(np.linalg.norm(action - avg_prev_action))
-        #     exploration_bonus = 0.05 * novelty
-        # else:
-        #     exploration_bonus = 0
-
-        # self.prev_action_history[self.history_idx] = action
-        # self.history_idx = (self.history_idx + 1) % 50
-
-
-        total = (
-            # progress_reward  + 
-                alive_bonus + 
-                heading_progress_reward +
-                # lateral_penalty +
-                # action_penalty +
-                smoothness_penalty +
-                posture_penalty
-                + ang_vel_penalty
-                + height_penalty
-                # + stance_collapse_penalty
-                #  + cross_leg_penalty
-                #  + exploration_bonus
-                # + height_down_penalty
-                 )
-        
-        now = time.time()
-        if self.reward_debug_interval_sec > 0 and now - self._reward_debug_last_time >= self.reward_debug_interval_sec:
-            self._reward_debug_last_time = now
-            self._reward_debug_steps_left = max(1, self.reward_debug_burst_steps)
-
-        if self._reward_debug_steps_left > 0:
-            self._reward_debug_steps_left -= 1
-            self.debug_log(
-                f"height_penalty:{height_penalty:.4f},"
-                f"smoothness_penalty:{smoothness_penalty:.4f},"
-                f"posture_penalty:{posture_penalty:.4f},"
-                f"heading_progress_reward:{heading_progress_reward:.4f},"
-                # f"stance_collapse_penalty:{stance_collapse_penalty:.4f},"
-                # f"cross_leg_penalty:{cross_leg_penalty:.4f},"
-                f"ang_vel_penalty:{ang_vel_penalty:.4f},"
-                #   f"height_down_penalty:{height_down_penalty:.4f}",
-                #   f"exploration_bonus:{exploration_bonus:.4f}"
-                f"alive_bonus:{alive_bonus:.4f},"
-                f"abs_yaw_error:{abs_yaw_error:.4f}"
-                f"total:{total:.4f}"
-                )
-
-        return total
-
-
-
-    def step(self, action):
-
-        r = self.Player.robot
-        max_action_delta =  0.1# Limit how much the action can change from the previous step to encourage smoother motions.
-        if self.previous_action is not None:
-            action = np.clip(action, self.previous_action - max_action_delta, self.previous_action + max_action_delta)
-        self.previous_action = action.copy()
-
-        self.target_joint_positions = (
-                # self.joint_nominal_position +
-                 self.scaling_factor * action
-        )
-        self.target_joint_positions *= self.train_sim_flip
-
-        for idx, target in enumerate(self.target_joint_positions):
-            r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=40, kd=1.2
-            )
-
-        self.previous_action = action.copy()
-
-        self.sync()  # run simulation step
-        self.step_counter += 1
-
-        if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
-            self.debug_joint_status()
-
-        current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
-
-        # Compute reward based on movement from previous step
-        reward = self.compute_reward(self.previous_pos, current_pos, action)
-
-        # Update previous position
-        self.previous_pos = current_pos.copy()
-        self.last_action_for_reward = action.copy()
-
-        # Fall detection and penalty
-        is_fallen = self.Player.world.global_position[2] < 0.55
-
-        # terminal state: the robot is falling or timeout
-        terminated = is_fallen or self.step_counter > 800 or self.route_completed
-        truncated = False
-
-        return self.observe(), reward, terminated, truncated, {}
-
-
-class Train(Train_Base):
-    def __init__(self, script) -> None:
-        super().__init__(script)
-
-    def train(self, args):
-
-        # --------------------------------------- Learning parameters
-        n_envs = int(os.environ.get("GYM_CPU_N_ENVS", "20"))
-        if n_envs < 1:
-            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
-        server_warmup_sec = float(os.environ.get("GYM_CPU_SERVER_WARMUP_SEC", "3.0"))
-        n_steps_per_env = int(os.environ.get("GYM_CPU_TRAIN_STEPS_PER_ENV", "512"))  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = int(os.environ.get("GYM_CPU_TRAIN_BATCH_SIZE", "512"))  # should be a factor of (n_steps_per_env * n_envs)
-        total_steps = 30000000
-        learning_rate = float(os.environ.get("GYM_CPU_TRAIN_LR", "3e-4"))
-        folder_name = f'Turn_R{self.robot_type}'
-        model_path = f'./scripts/gyms/logs/{folder_name}/'
-
-        print(f"Model path: {model_path}")
-        print(f"Using {n_envs} parallel environments")
-
-        # --------------------------------------- Run algorithm
-        def init_env(i_env, monitor=False):
-            def thunk():
-                env = WalkEnv(self.ip, self.server_p + i_env)
-                if monitor:
-                    env = Monitor(env)
-                return env
-
-            return thunk
-
-        server_log_dir = os.path.join(model_path, "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1, no_render=True, no_realtime=True)  # include 1 extra server for testing
-
-        # Wait for servers to start
-        print(f"Starting {n_envs + 1} rcssservermj servers...")
-        if server_warmup_sec > 0:
-            print(f"Waiting {server_warmup_sec:.1f}s for server warmup...")
-            sleep(server_warmup_sec)
-        print("Servers started, creating environments...")
-
-        env = SubprocVecEnv([init_env(i, monitor=True) for i in range(n_envs)], start_method="spawn")
-        # Use single-process eval env to avoid extra subprocess fragility during callback evaluation.
-        eval_env = DummyVecEnv([init_env(n_envs, monitor=True)])
-
-        try:
-            # Custom policy network architecture
-            policy_kwargs = dict(
-                net_arch=dict(
-                    pi=[512, 256, 128],  # Policy network: 3 layers
-                    vf=[512, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
-            )
-
-            if "model_file" in args:  # retrain
-                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
-                                 batch_size=minibatch_size, learning_rate=learning_rate)
-            else:  # train new model
-                model = PPO(
-                    "MlpPolicy",
-                    env=env,
-                    verbose=1,
-                    n_steps=n_steps_per_env,
-                    batch_size=minibatch_size,
-                    learning_rate=learning_rate,
-                    device="cpu",
-                    policy_kwargs=policy_kwargs,
-                    ent_coef=float(os.environ.get("GYM_CPU_TRAIN_ENT_COEF", "0.05")),  # Entropy coefficient for exploration
-                    clip_range=float(os.environ.get("GYM_CPU_TRAIN_CLIP_RANGE", "0.2")),  # PPO clipping parameter
-                    gae_lambda=0.95,  # GAE lambda
-                    gamma=float(os.environ.get("GYM_CPU_TRAIN_GAMMA", "0.95")), # Discount factor
-                    # target_kl=0.03,
-                    n_epochs=int(os.environ.get("GYM_CPU_TRAIN_EPOCHS", "5")),
-                    tensorboard_log=f"./scripts/gyms/logs/{folder_name}/tensorboard/",
-                    max_grad_norm=float(os.environ.get("GYM_CPU_TRAIN_MAX_GRAD_NORM", "0.5"))
-                )
-
-            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
-                                          eval_freq=n_steps_per_env * 20, save_freq=n_steps_per_env * 20, eval_eps=7,
-                                          backup_env_file=__file__)
-        except KeyboardInterrupt:
-            sleep(1)  # wait for child processes
-            print("\nctrl+c pressed, aborting...\n")
-            servers.kill()
-            return
-
-        env.close()
-        eval_env.close()
-        servers.kill()
-
-    def test(self, args):
-
-        # Uses different server and monitor ports
-        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        test_no_render = os.environ.get("GYM_CPU_TEST_NO_RENDER", "0") == "1"
-        test_no_realtime = os.environ.get("GYM_CPU_TEST_NO_REALTIME", "0") == "1"
-
-        server = Train_Server(
-            self.server_p - 1,
-            self.monitor_p,
-            1,
-            no_render=test_no_render,
-            no_realtime=test_no_realtime,
-        )
-        env = WalkEnv(self.ip, self.server_p - 1)
-        model = PPO.load(args["model_file"], env=env)
-
-        try:
-            self.export_model(args["model_file"], args["model_file"] + ".pkl",
-                              False)  # Export to pkl to create custom behavior
-            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
-        except KeyboardInterrupt:
-            print()
-
-        env.close()
-        server.kill()
-
-
-if __name__ == "__main__":
-    from types import SimpleNamespace
-
-    # 创建默认参数
-    script_args = SimpleNamespace(
-        args=SimpleNamespace(
-            i='127.0.0.1',  # Server IP
-            p=3100,  # Server port
-            m=3200,  # Monitor port
-            r=0,  # Robot type
-            t='Gym',  # Team name
-            u=1  # Uniform number
-        )
-    )
-
-    trainer = Train(script_args)
-
-    run_mode = os.environ.get("GYM_CPU_MODE", "train").strip().lower()
-
-    if run_mode == "test":
-        test_model_file = os.environ.get("GYM_CPU_TEST_MODEL", "scripts/gyms/logs/Turn_R0_004/best_model.zip")
-        test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Turn_R0_004/")
-        trainer.test({"model_file": test_model_file, "folder_dir": test_folder})
-    else:
-        retrain_model = os.environ.get("GYM_CPU_TRAIN_MODEL", "").strip()
-        if retrain_model:
-            trainer.train({"model_file": retrain_model})
-        else:
-            trainer.train({})

scripts/gyms/logs/Turn_R0_005/Walk.py

@@ -1,755 +0,0 @@
-import os
-import numpy as np
-import math
-import time
-from time import sleep
-from random import random
-from random import uniform
-from itertools import count
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.monitor import Monitor
-from stable_baselines3.common.vec_env import SubprocVecEnv, DummyVecEnv
-
-import gymnasium as gym
-from gymnasium import spaces
-
-from scripts.commons.Train_Base import Train_Base
-from scripts.commons.Server import Server as Train_Server
-
-from agent.base_agent import Base_Agent
-from utils.math_ops import MathOps
-
-from scipy.spatial.transform import Rotation as R
-
-'''
-Objective:
-Learn how to run forward using step primitive
-----------
-- class Basic_Run: implements an OpenAI custom gym
-- class Train:  implements algorithms to train a new model or test an existing model
-'''
-
-
-class WalkEnv(gym.Env):
-    def __init__(self, ip, server_p) -> None:
-
-        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
-        self.Player = player = Base_Agent(
-            team_name="Gym",
-            number=1,
-            host=ip,
-            port=server_p
-        )
-        self.robot_type = self.Player.robot
-        self.step_counter = 0  # to limit episode size
-        self.force_play_on = True
-
-        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
-        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
-        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
-        self.isfallen = False
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.debug_every_n_steps = 5
-        self.enable_debug_joint_status = False
-        self.reward_debug_interval_sec = float(os.environ.get("GYM_CPU_REWARD_DEBUG_INTERVAL_SEC", "600"))
-        self.reward_debug_burst_steps = int(os.environ.get("GYM_CPU_REWARD_DEBUG_BURST_STEPS", "10"))
-        self._reward_debug_last_time = time.time()
-        self._reward_debug_steps_left = 0
-        self.calibrate_nominal_from_neutral = True
-        self.auto_calibrate_train_sim_flip = True
-        self.nominal_calibrated_once = False
-        self.flip_calibrated_once = False
-        self._target_hz = 0.0
-        self._target_dt = 0.0
-        self._last_sync_time = None
-        target_hz_env = 0
-        if target_hz_env:
-            try:
-                self._target_hz = float(target_hz_env)
-            except ValueError:
-                self._target_hz = 0.0
-        if self._target_hz > 0.0:
-            self._target_dt = 1.0 / self._target_hz
-
-        # State space
-        # 原始观测大小: 78
-        obs_size = 78
-        self.obs = np.zeros(obs_size, np.float32)
-        self.observation_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(obs_size,),
-            dtype=np.float32
-        )
-
-        action_dim = len(self.Player.robot.ROBOT_MOTORS)
-        self.no_of_actions = action_dim
-        self.action_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(action_dim,),
-            dtype=np.float32
-        )
-
-        # 中立姿态
-        self.joint_nominal_position = np.array(
-            [
-                0.0,
-                0.0,
-                0.0,
-                1.4,
-                0.0,
-                -0.4,
-                0.0,
-                -1.4,
-                0.0,
-                0.4,
-                0.0,
-                -0.4,
-                0.0,
-                0.0,
-                0.8,
-                -0.4,
-                0.0,
-                0.4,
-                0.0,
-                0.0,
-                -0.8,
-                0.4,
-                0.0,
-            ]
-        )
-        self.joint_nominal_position = np.zeros(self.no_of_actions)
-        self.train_sim_flip = np.array(
-            [
-                1.0,  # 0: Head_yaw (he1)
-                -1.0,  # 1: Head_pitch (he2)
-                1.0,  # 2: Left_Shoulder_Pitch (lae1)
-                -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                -1.0,  # 4: Left_Elbow_Pitch (lae3)
-                1.0,  # 5: Left_Elbow_Yaw (lae4)
-                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                -1.0,  # 7: Right_Shoulder_Roll (rae2)
-                1.0,  # 8: Right_Elbow_Pitch (rae3)
-                1.0,  # 9: Right_Elbow_Yaw (rae4)
-                1.0,  # 10: Waist (te1)
-                1.0,  # 11: Left_Hip_Pitch (lle1)
-                -1.0,  # 12: Left_Hip_Roll (lle2)
-                -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,  # 14: Left_Knee_Pitch (lle4)
-                1.0,  # 15: Left_Ankle_Pitch (lle5)
-                -1.0,  # 16: Left_Ankle_Roll (lle6)
-                -1.0,  # 17: Right_Hip_Pitch (rle1)
-                -1.0,  # 18: Right_Hip_Roll (rle2)
-                -1.0,  # 19: Right_Hip_Yaw (rle3)
-                -1.0,  # 20: Right_Knee_Pitch (rle4)
-                -1.0,  # 21: Right_Ankle_Pitch (rle5)
-                -1.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-
-        self.scaling_factor = 0.3
-        # self.scaling_factor = 1
-
-        # Encourage a minimum lateral stance so the policy avoids feet overlap.
-        self.min_stance_rad = 0.10
-
-        # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = False
-        self.reset_beam_yaw_range_deg = float(os.environ.get("GYM_CPU_RESET_BEAM_YAW_RANGE_DEG", "180"))
-        self.reset_target_bearing_range_deg = float(os.environ.get("GYM_CPU_RESET_TARGET_BEARING_RANGE_DEG", "45"))
-        self.reset_target_distance_min = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MIN", "1.2"))
-        self.reset_target_distance_max = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MAX", "2.8"))
-        if self.reset_target_distance_min > self.reset_target_distance_max:
-            self.reset_target_distance_min, self.reset_target_distance_max = (
-                self.reset_target_distance_max,
-                self.reset_target_distance_min,
-            )
-        self.reset_joint_noise_rad = 0.025
-        self.reset_perturb_steps = 4
-        self.reset_recover_steps = 8
-
-        self.reward_smoothness_scale = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_SCALE", "0.06"))
-        self.reward_smoothness_cap = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_CAP", "0.45"))
-        self.reward_head_toward_bonus = float(os.environ.get("GYM_CPU_REWARD_HEAD_TOWARD_BONUS", "1"))
-
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Track previous position
-        self.last_yaw_error = None
-        self.Player.server.connect()
-        # sleep(2.0)  # Longer wait for connection to establish completely
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-        self.start_time = time.time()
-
-    def _reconnect_server(self):
-        try:
-            self.Player.server.shutdown()
-        except Exception:
-            pass
-
-        self.Player.server.connect()
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-
-    def _safe_receive_world_update(self, retries=1):
-        last_exc = None
-        for attempt in range(retries + 1):
-            try:
-                self.Player.server.receive()
-                self.Player.world.update()
-                return
-            except (ConnectionResetError, OSError) as exc:
-                last_exc = exc
-                if attempt >= retries:
-                    raise
-                self._reconnect_server()
-        if last_exc is not None:
-            raise last_exc
-
-    def debug_log(self, message):
-        print(message)
-        try:
-            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
-            with open(log_path, "a", encoding="utf-8") as f:
-                f.write(message + "\n")
-        except OSError:
-            pass
-
-    @staticmethod
-    def _wrap_to_pi(angle_rad: float) -> float:
-        return (angle_rad + math.pi) % (2.0 * math.pi) - math.pi
-
-    def observe(self, init=False):
-
-        """获取当前观测值"""
-        robot = self.Player.robot
-        world = self.Player.world
-
-        # Safety check: ensure data is available
-
-        # 计算目标速度
-        raw_target = self.target_position - world.global_position[:2]
-        velocity = MathOps.rotate_2d_vec(
-            raw_target,
-            -robot.global_orientation_euler[2],
-            is_rad=False
-        )
-
-        # 计算相对方向
-        rel_orientation = MathOps.vector_angle(velocity) * 0.3
-        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
-
-        velocity = np.concatenate([velocity, np.array([rel_orientation])])
-        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
-        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
-
-        # 关节状态
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        qpos_qvel_previous_action = np.concatenate([
-            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
-            radian_joint_speeds / 110.0 * self.train_sim_flip,
-            self.previous_action / 10.0,
-        ])
-
-        # 角速度
-        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
-
-        # 投影的重力方向
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-
-        # 组合观测
-        observation = np.concatenate([
-            qpos_qvel_previous_action,
-            ang_vel,
-            velocity,
-            projected_gravity,
-        ])
-
-        observation = np.clip(observation, -10.0, 10.0)
-        return observation.astype(np.float32)
-
-    def sync(self):
-        ''' Run a single simulation step '''
-        self._safe_receive_world_update(retries=1)
-        self.Player.robot.commit_motor_targets_pd()
-        self.Player.server.send()
-        if self._target_dt > 0.0:
-            now = time.time()
-            if self._last_sync_time is None:
-                self._last_sync_time = now
-                return
-            elapsed = now - self._last_sync_time
-            remaining = self._target_dt - elapsed
-            if remaining > 0.0:
-                time.sleep(remaining)
-                now = time.time()
-            self._last_sync_time = now
-
-    def debug_joint_status(self):
-        robot = self.Player.robot
-        actual_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        target_joint_positions = getattr(
-            self,
-            'target_joint_positions',
-            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
-        )
-        joint_error = actual_joint_positions - target_joint_positions
-        leg_slice = slice(11, None)
-
-        self.debug_log(
-            "[WalkDebug] "
-            f"step={self.step_counter} "
-            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
-            f"target_xy={np.round(self.target_position, 3).tolist()} "
-            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
-            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
-            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
-            f"fallen={self.Player.world.global_position[2] < 0.3}"
-        )
-        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
-
-    def reset(self, seed=None, options=None):
-        '''
-        Reset and stabilize the robot
-        Note: for some behaviors it would be better to reduce stabilization or add noise
-        '''
-        r = self.Player.robot
-        super().reset(seed=seed)
-        if seed is not None:
-            np.random.seed(seed)
-
-        target_distance = np.random.uniform(self.reset_target_distance_min, self.reset_target_distance_max)
-        target_bearing_deg = np.random.uniform(-self.reset_target_bearing_range_deg, self.reset_target_bearing_range_deg)
-
-        self.step_counter = 0
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
-        self.last_yaw_error = None
-        self.walk_cycle_step = 0
-        self._reward_debug_steps_left = 0
-
-        # 随机 beam 目标位置和朝向，增加训练多样性
-        beam_x = (random() - 0.5) * 10
-        beam_y = (random() - 0.5) * 10
-        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
-
-        for _ in range(5):
-            self._safe_receive_world_update(retries=2)
-            self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
-            self.Player.server.send()
-
-        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
-        finished_count = 0
-        for _ in range(50):
-            finished = self.Player.skills_manager.execute("Neutral")
-            self.sync()
-            if finished:
-                finished_count += 1
-                if finished_count >= 20:  # 假设需要连续20次完成才算成功
-                    break
-
-        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-            # Perturb waist + lower body only (10:), keep head/arms stable.
-            perturb_action[10:] = np.random.uniform(
-                -self.reset_joint_noise_rad,
-                self.reset_joint_noise_rad,
-                size=(self.no_of_actions - 10,)
-            )
-
-            for _ in range(self.reset_perturb_steps):
-                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-            for i in range(self.reset_recover_steps):
-                # Linearly fade perturbation to help policy start from near-neutral.
-                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-        # memory variables
-        self.sync()  
-        self.initial_position = np.array(self.Player.world.global_position[:2])
-        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions, np.float32)
-        # Randomize global target bearing so policy must learn to rotate toward it first.
-        heading_deg = float(r.global_orientation_euler[2])
-        target_offset = MathOps.rotate_2d_vec(
-            np.array([target_distance, 0.0]),
-            heading_deg + target_bearing_deg,
-            is_rad=False,
-        )
-        point1 = self.initial_position + target_offset
-        self.point_list = [point1]
-        self.target_position = self.point_list[self.waypoint_index]
-        self.initial_height = self.Player.world.global_position[2]
-
-        return self.observe(True), {}
-
-    def render(self, mode='human', close=False):
-        return
-
-    
-    def compute_reward(self, previous_pos, current_pos, action):
-        height = float(self.Player.world.global_position[2])
-        robot = self.Player.robot
-
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-        tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        ang_vel = np.deg2rad(robot.gyroscope)
-        rp_ang_vel_mag = float(np.linalg.norm(ang_vel[:2]))
-
-        is_fallen = height < 0.55
-        if is_fallen:
-            # remain = max(0, 800 - self.step_counter)
-            # return -8.0 - 0.01 * remain
-            return -1.0
-        
-
-
-
-        # Turn-to-target shaping.
-        to_target = self.target_position - current_pos
-        dist_to_target = float(np.linalg.norm(to_target))
-        if dist_to_target > 1e-6:
-            target_yaw = math.atan2(float(to_target[1]), float(to_target[0]))
-        else:
-            target_yaw = 0.0
-
-        robot_yaw = math.radians(float(robot.global_orientation_euler[2]))
-        yaw_error = target_yaw - robot_yaw
-
-        # Main heading objective: face the target direction.
-        # heading_align_reward = 1.0 * math.cos(yaw_error)
-
-        abs_yaw_error = abs(yaw_error)
-        alive_bonus = 2.0 * max(0.0, 1.0 - abs_yaw_error / math.pi)
-
-        if self.last_yaw_error is None:
-            heading_progress_reward = 0.0
-        else:
-            prev_abs_yaw_error = abs(self.last_yaw_error)
-            yaw_err_delta = prev_abs_yaw_error - abs_yaw_error
-            progress_gate = 1.0 if abs_yaw_error > math.radians(4.0) else 0.0
-            heading_progress_reward =  progress_gate * yaw_err_delta
-            heading_progress_reward = float(np.clip(heading_progress_reward, -0.7, 0.7))
-        self.last_yaw_error = yaw_error
-
-        # action_penalty = -0.01 * float(np.linalg.norm(action))
-        smoothness_penalty = -0.02 * float(np.linalg.norm(action - self.last_action_for_reward))
-
-        posture_penalty = -0.6 * tilt_mag
-        # Penalize roll/pitch rotational shake but do not penalize yaw turning directly.
-        ang_vel_penalty = -0.06 * rp_ang_vel_mag
-
-        joint_pos = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        ) * self.train_sim_flip
-        left_hip_roll = float(joint_pos[12])
-        right_hip_roll = float(joint_pos[18])
-        left_ankle_roll = float(joint_pos[16])
-        right_ankle_roll = float(joint_pos[22])
-
-        hip_spread = left_hip_roll - right_hip_roll if right_hip_roll > 0.03 and left_hip_roll > 0.03 else 0.0
-        ankle_spread = left_ankle_roll - right_ankle_roll if right_ankle_roll > 0.03 and left_ankle_roll > 0.03 else 0.0
-        stance_metric = 0.6 * abs(hip_spread) + 0.4 * abs(ankle_spread)
-
-        # Penalize narrow stance (feet too close) and scissoring (cross-leg pattern).
-        stance_collapse_penalty = -4.0 * max(0.0, self.min_stance_rad - stance_metric) 
-        cross_leg_penalty = -1.2 * max(0.0, -(hip_spread * ankle_spread))
-
-        target_height = self.initial_height
-        height_error =  height - target_height
-        height_error = height - target_height
-        
-        height_penalty = -(math.exp(12*abs(height_error))-1) if height_error > 0.04 else 0
-
-        # # 在 compute_reward 开头附近，添加高度变化率计算
-        # if not hasattr(self, 'last_height'):
-        #     self.last_height = height
-        #     self.last_height_time = self.step_counter  # 可选，用于时间间隔
-        # height_rate = height - self.last_height  # 正为上升，负为下降
-        # self.last_height = height
-
-        # 惩罚高度下降（负变化率）
-        # height_down_penalty = -5.0 * max(0, -height_rate)  # 系数可调，-height_rate 为正表示下降幅度
-
-        # # 在 compute_reward 中
-        # if self.step_counter > 50:
-        #     avg_prev_action = np.mean(self.prev_action_history, axis=0)
-        #     novelty = float(np.linalg.norm(action - avg_prev_action))
-        #     exploration_bonus = 0.05 * novelty
-        # else:
-        #     exploration_bonus = 0
-
-        # self.prev_action_history[self.history_idx] = action
-        # self.history_idx = (self.history_idx + 1) % 50
-
-
-        total = (
-            # progress_reward  + 
-                alive_bonus + 
-                heading_progress_reward +
-                # lateral_penalty +
-                # action_penalty +
-                smoothness_penalty +
-                posture_penalty
-                + ang_vel_penalty
-                + height_penalty
-                # + stance_collapse_penalty
-                #  + cross_leg_penalty
-                #  + exploration_bonus
-                # + height_down_penalty
-                 )
-        # print(height_error, height_penalty)
-        
-        now = time.time()
-        if self.reward_debug_interval_sec > 0 and now - self._reward_debug_last_time >= self.reward_debug_interval_sec:
-            self._reward_debug_last_time = now
-            self._reward_debug_steps_left = max(1, self.reward_debug_burst_steps)
-
-        if self._reward_debug_steps_left > 0:
-            self._reward_debug_steps_left -= 1
-            self.debug_log(
-                f"height_penalty:{height_penalty:.4f},"
-                f"smoothness_penalty:{smoothness_penalty:.4f},"
-                f"posture_penalty:{posture_penalty:.4f},"
-                f"heading_progress_reward:{heading_progress_reward:.4f},"
-                # f"stance_collapse_penalty:{stance_collapse_penalty:.4f},"
-                # f"cross_leg_penalty:{cross_leg_penalty:.4f},"
-                f"ang_vel_penalty:{ang_vel_penalty:.4f},"
-                #   f"height_down_penalty:{height_down_penalty:.4f}",
-                #   f"exploration_bonus:{exploration_bonus:.4f}"
-                f"alive_bonus:{alive_bonus:.4f},"
-                f"abs_yaw_error:{abs_yaw_error:.4f}"
-                f"total:{total:.4f}"
-                )
-
-        return total
-
-
-
-    def step(self, action):
-
-        r = self.Player.robot
-        max_action_delta =  0.1# Limit how much the action can change from the previous step to encourage smoother motions.
-        if self.previous_action is not None:
-            action = np.clip(action, self.previous_action - max_action_delta, self.previous_action + max_action_delta)
-        self.previous_action = action.copy()
-
-        self.target_joint_positions = (
-                # self.joint_nominal_position +
-                 self.scaling_factor * action
-        )
-        self.target_joint_positions *= self.train_sim_flip
-
-        for idx, target in enumerate(self.target_joint_positions):
-            r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=40, kd=1.2
-            )
-
-        self.previous_action = action.copy()
-
-        self.sync()  # run simulation step
-        self.step_counter += 1
-
-        if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
-            self.debug_joint_status()
-
-        current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
-
-        # Compute reward based on movement from previous step
-        reward = self.compute_reward(self.previous_pos, current_pos, action)
-
-        # Update previous position
-        self.previous_pos = current_pos.copy()
-        self.last_action_for_reward = action.copy()
-
-        # Fall detection and penalty
-        is_fallen = self.Player.world.global_position[2] < 0.55
-
-        # terminal state: the robot is falling or timeout
-        terminated = is_fallen or self.step_counter > 800 or self.route_completed
-        truncated = False
-
-        return self.observe(), reward, terminated, truncated, {}
-
-
-class Train(Train_Base):
-    def __init__(self, script) -> None:
-        super().__init__(script)
-
-    def train(self, args):
-
-        # --------------------------------------- Learning parameters
-        n_envs = int(os.environ.get("GYM_CPU_N_ENVS", "20"))
-        if n_envs < 1:
-            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
-        server_warmup_sec = float(os.environ.get("GYM_CPU_SERVER_WARMUP_SEC", "3.0"))
-        n_steps_per_env = int(os.environ.get("GYM_CPU_TRAIN_STEPS_PER_ENV", "512"))  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = int(os.environ.get("GYM_CPU_TRAIN_BATCH_SIZE", "512"))  # should be a factor of (n_steps_per_env * n_envs)
-        total_steps = 30000000
-        learning_rate = float(os.environ.get("GYM_CPU_TRAIN_LR", "3e-4"))
-        folder_name = f'Turn_R{self.robot_type}'
-        model_path = f'./scripts/gyms/logs/{folder_name}/'
-
-        print(f"Model path: {model_path}")
-        print(f"Using {n_envs} parallel environments")
-
-        # --------------------------------------- Run algorithm
-        def init_env(i_env, monitor=False):
-            def thunk():
-                env = WalkEnv(self.ip, self.server_p + i_env)
-                if monitor:
-                    env = Monitor(env)
-                return env
-
-            return thunk
-
-        server_log_dir = os.path.join(model_path, "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1, no_render=True, no_realtime=True)  # include 1 extra server for testing
-
-        # Wait for servers to start
-        print(f"Starting {n_envs + 1} rcssservermj servers...")
-        if server_warmup_sec > 0:
-            print(f"Waiting {server_warmup_sec:.1f}s for server warmup...")
-            sleep(server_warmup_sec)
-        print("Servers started, creating environments...")
-
-        env = SubprocVecEnv([init_env(i, monitor=True) for i in range(n_envs)], start_method="spawn")
-        # Use single-process eval env to avoid extra subprocess fragility during callback evaluation.
-        eval_env = DummyVecEnv([init_env(n_envs, monitor=True)])
-
-        try:
-            # Custom policy network architecture
-            policy_kwargs = dict(
-                net_arch=dict(
-                    pi=[512, 256, 128],  # Policy network: 3 layers
-                    vf=[512, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
-            )
-
-            if "model_file" in args:  # retrain
-                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
-                                 batch_size=minibatch_size, learning_rate=learning_rate)
-            else:  # train new model
-                model = PPO(
-                    "MlpPolicy",
-                    env=env,
-                    verbose=1,
-                    n_steps=n_steps_per_env,
-                    batch_size=minibatch_size,
-                    learning_rate=learning_rate,
-                    device="cpu",
-                    policy_kwargs=policy_kwargs,
-                    ent_coef=float(os.environ.get("GYM_CPU_TRAIN_ENT_COEF", "0.05")),  # Entropy coefficient for exploration
-                    clip_range=float(os.environ.get("GYM_CPU_TRAIN_CLIP_RANGE", "0.2")),  # PPO clipping parameter
-                    gae_lambda=0.95,  # GAE lambda
-                    gamma=float(os.environ.get("GYM_CPU_TRAIN_GAMMA", "0.95")), # Discount factor
-                    # target_kl=0.03,
-                    n_epochs=int(os.environ.get("GYM_CPU_TRAIN_EPOCHS", "5")),
-                    tensorboard_log=f"./scripts/gyms/logs/{folder_name}/tensorboard/",
-                    max_grad_norm=float(os.environ.get("GYM_CPU_TRAIN_MAX_GRAD_NORM", "0.5"))
-                )
-
-            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
-                                          eval_freq=n_steps_per_env * 20, save_freq=n_steps_per_env * 20, eval_eps=7,
-                                          backup_env_file=__file__)
-        except KeyboardInterrupt:
-            sleep(1)  # wait for child processes
-            print("\nctrl+c pressed, aborting...\n")
-            servers.kill()
-            return
-
-        env.close()
-        eval_env.close()
-        servers.kill()
-
-    def test(self, args):
-
-        # Uses different server and monitor ports
-        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        test_no_render = os.environ.get("GYM_CPU_TEST_NO_RENDER", "0") == "1"
-        test_no_realtime = os.environ.get("GYM_CPU_TEST_NO_REALTIME", "0") == "1"
-
-        server = Train_Server(
-            self.server_p - 1,
-            self.monitor_p,
-            1,
-            no_render=test_no_render,
-            no_realtime=test_no_realtime,
-        )
-        env = WalkEnv(self.ip, self.server_p - 1)
-        model = PPO.load(args["model_file"], env=env)
-
-        try:
-            self.export_model(args["model_file"], args["model_file"] + ".pkl",
-                              False)  # Export to pkl to create custom behavior
-            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
-        except KeyboardInterrupt:
-            print()
-
-        env.close()
-        server.kill()
-
-
-if __name__ == "__main__":
-    from types import SimpleNamespace
-
-    # 创建默认参数
-    script_args = SimpleNamespace(
-        args=SimpleNamespace(
-            i='127.0.0.1',  # Server IP
-            p=3100,  # Server port
-            m=3200,  # Monitor port
-            r=0,  # Robot type
-            t='Gym',  # Team name
-            u=1  # Uniform number
-        )
-    )
-
-    trainer = Train(script_args)
-
-    run_mode = os.environ.get("GYM_CPU_MODE", "train").strip().lower()
-
-    if run_mode == "test":
-        test_model_file = os.environ.get("GYM_CPU_TEST_MODEL", "scripts/gyms/logs/Turn_R0_004/best_model.zip")
-        test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Turn_R0_004/")
-        trainer.test({"model_file": test_model_file, "folder_dir": test_folder})
-    else:
-        retrain_model = os.environ.get("GYM_CPU_TRAIN_MODEL", "").strip()
-        if retrain_model:
-            trainer.train({"model_file": retrain_model})
-        else:
-            trainer.train({})

scripts/gyms/logs/Turn_R0_006/Walk.py

@@ -1,821 +0,0 @@
-import os
-import numpy as np
-import math
-import time
-from time import sleep
-from random import random
-from random import uniform
-from itertools import count
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.monitor import Monitor
-from stable_baselines3.common.vec_env import SubprocVecEnv, DummyVecEnv
-
-import gymnasium as gym
-from gymnasium import spaces
-
-from scripts.commons.Train_Base import Train_Base
-from scripts.commons.Server import Server as Train_Server
-
-from agent.base_agent import Base_Agent
-from utils.math_ops import MathOps
-
-from scipy.spatial.transform import Rotation as R
-
-'''
-Objective:
-Learn how to run forward using step primitive
-----------
-- class Basic_Run: implements an OpenAI custom gym
-- class Train:  implements algorithms to train a new model or test an existing model
-'''
-
-
-class WalkEnv(gym.Env):
-    def __init__(self, ip, server_p) -> None:
-
-        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
-        self.Player = player = Base_Agent(
-            team_name="Gym",
-            number=1,
-            host=ip,
-            port=server_p
-        )
-        self.robot_type = self.Player.robot
-        self.step_counter = 0  # to limit episode size
-        self.force_play_on = True
-
-        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
-        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
-        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
-        self.isfallen = False
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.debug_every_n_steps = 5
-        self.enable_debug_joint_status = False
-        self.reward_debug_interval_sec = float(os.environ.get("GYM_CPU_REWARD_DEBUG_INTERVAL_SEC", "600"))
-        self.reward_debug_burst_steps = int(os.environ.get("GYM_CPU_REWARD_DEBUG_BURST_STEPS", "10"))
-        self._reward_debug_last_time = time.time()
-        self._reward_debug_steps_left = 0
-        self.calibrate_nominal_from_neutral = True
-        self.auto_calibrate_train_sim_flip = True
-        self.nominal_calibrated_once = False
-        self.flip_calibrated_once = False
-        self._target_hz = 0.0
-        self._target_dt = 0.0
-        self._last_sync_time = None
-        target_hz_env = 0
-        if target_hz_env:
-            try:
-                self._target_hz = float(target_hz_env)
-            except ValueError:
-                self._target_hz = 0.0
-        if self._target_hz > 0.0:
-            self._target_dt = 1.0 / self._target_hz
-
-        # State space
-        # 原始观测大小: 78
-        obs_size = 78
-        self.obs = np.zeros(obs_size, np.float32)
-        self.observation_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(obs_size,),
-            dtype=np.float32
-        )
-
-        action_dim = len(self.Player.robot.ROBOT_MOTORS)
-        self.no_of_actions = action_dim
-        self.action_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(action_dim,),
-            dtype=np.float32
-        )
-
-        # 中立姿态
-        self.joint_nominal_position = np.array(
-            [
-                0.0,  # 0: Head_yaw (he1)
-                0.0,  # 1: Head_pitch (he2)
-                0.0,  # 2: Left_Shoulder_Pitch (lae1)
-                0.0,  # 3: Left_Shoulder_Roll (lae2)
-                0.0,  # 4: Left_Elbow_Pitch (lae3)
-                0.0,  # 5: Left_Elbow_Yaw (lae4)
-                0.0,  # 6: Right_Shoulder_Pitch (rae1)
-                0.0,  # 7: Right_Shoulder_Roll (rae2)
-                0.0,  # 8: Right_Elbow_Pitch (rae3)
-                0.0,  # 9: Right_Elbow_Yaw (rae4)
-                0.0,  # 10: Waist (te1)
-                0.0,  # 11: Left_Hip_Pitch (lle1)
-                0.0,  # 12: Left_Hip_Roll (lle2)
-                1.0,  # 13: Left_Hip_Yaw (lle3)
-                0.0,  # 14: Left_Knee_Pitch (lle4)
-                0.0,  # 15: Left_Ankle_Pitch (lle5)
-                0.0,  # 16: Left_Ankle_Roll (lle6)
-                0.0,  # 17: Right_Hip_Pitch (rle1)
-                0.0,  # 18: Right_Hip_Roll (rle2)
-                1.0,  # 19: Right_Hip_Yaw (rle3)
-                0.0,  # 20: Right_Knee_Pitch (rle4)
-                0.0,  # 21: Right_Ankle_Pitch (rle5)
-                0.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-        self.joint_nominal_position = np.zeros(self.no_of_actions)
-        self.train_sim_flip = np.array(
-            [
-                1.0,  # 0: Head_yaw (he1)
-                -1.0,  # 1: Head_pitch (he2)
-                1.0,  # 2: Left_Shoulder_Pitch (lae1)
-                -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                -1.0,  # 4: Left_Elbow_Pitch (lae3)
-                1.0,  # 5: Left_Elbow_Yaw (lae4)
-                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                -1.0,  # 7: Right_Shoulder_Roll (rae2)
-                1.0,  # 8: Right_Elbow_Pitch (rae3)
-                1.0,  # 9: Right_Elbow_Yaw (rae4)
-                1.0,  # 10: Waist (te1)
-                1.0,  # 11: Left_Hip_Pitch (lle1)
-                -1.0,  # 12: Left_Hip_Roll (lle2)
-                -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,  # 14: Left_Knee_Pitch (lle4)
-                1.0,  # 15: Left_Ankle_Pitch (lle5)
-                -1.0,  # 16: Left_Ankle_Roll (lle6)
-                -1.0,  # 17: Right_Hip_Pitch (rle1)
-                -1.0,  # 18: Right_Hip_Roll (rle2)
-                -1.0,  # 19: Right_Hip_Yaw (rle3)
-                -1.0,  # 20: Right_Knee_Pitch (rle4)
-                -1.0,  # 21: Right_Ankle_Pitch (rle5)
-                -1.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-
-        self.scaling_factor = 0.3
-        # self.scaling_factor = 1
-
-        # Encourage a minimum lateral stance so the policy avoids feet overlap.
-        self.min_stance_rad = 0.10
-
-        # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = False
-        self.reset_beam_yaw_range_deg = float(os.environ.get("GYM_CPU_RESET_BEAM_YAW_RANGE_DEG", "180"))
-        self.reset_target_bearing_range_deg = float(os.environ.get("GYM_CPU_RESET_TARGET_BEARING_RANGE_DEG", "45"))
-        self.reset_target_distance_min = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MIN", "1.2"))
-        self.reset_target_distance_max = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MAX", "2.8"))
-        if self.reset_target_distance_min > self.reset_target_distance_max:
-            self.reset_target_distance_min, self.reset_target_distance_max = (
-                self.reset_target_distance_max,
-                self.reset_target_distance_min,
-            )
-        self.reset_joint_noise_rad = 0.025
-        self.reset_perturb_steps = 4
-        self.reset_recover_steps = 8
-
-        self.reward_smoothness_scale = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_SCALE", "0.06"))
-        self.reward_smoothness_cap = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_CAP", "0.45"))
-        self.reward_head_toward_bonus = float(os.environ.get("GYM_CPU_REWARD_HEAD_TOWARD_BONUS", "1"))
-
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Track previous position
-        self.last_yaw_error = None
-        self.Player.server.connect()
-        # sleep(2.0)  # Longer wait for connection to establish completely
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-        self.start_time = time.time()
-
-    def _reconnect_server(self):
-        try:
-            self.Player.server.shutdown()
-        except Exception:
-            pass
-
-        self.Player.server.connect()
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-
-    def _safe_receive_world_update(self, retries=1):
-        last_exc = None
-        for attempt in range(retries + 1):
-            try:
-                self.Player.server.receive()
-                self.Player.world.update()
-                return
-            except (ConnectionResetError, OSError) as exc:
-                last_exc = exc
-                if attempt >= retries:
-                    raise
-                self._reconnect_server()
-        if last_exc is not None:
-            raise last_exc
-
-    def debug_log(self, message):
-        print(message)
-        try:
-            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
-            with open(log_path, "a", encoding="utf-8") as f:
-                f.write(message + "\n")
-        except OSError:
-            pass
-
-    @staticmethod
-    def _wrap_to_pi(angle_rad: float) -> float:
-        return (angle_rad + math.pi) % (2.0 * math.pi) - math.pi
-
-    def observe(self, init=False):
-
-        """获取当前观测值"""
-        robot = self.Player.robot
-        world = self.Player.world
-
-        # Safety check: ensure data is available
-
-        # 计算目标速度
-        raw_target = self.target_position - world.global_position[:2]
-        velocity = MathOps.rotate_2d_vec(
-            raw_target,
-            -robot.global_orientation_euler[2],
-            is_rad=False
-        )
-
-        # 计算相对方向
-        rel_orientation = MathOps.vector_angle(velocity) * 0.3
-        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
-
-        velocity = np.concatenate([velocity, np.array([rel_orientation])])
-        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
-        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
-
-        # 关节状态
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        qpos_qvel_previous_action = np.concatenate([
-            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
-            radian_joint_speeds / 110.0 * self.train_sim_flip,
-            self.previous_action / 10.0,
-        ])
-
-        # 角速度
-        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
-
-        # 投影的重力方向
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-
-        # 组合观测
-        observation = np.concatenate([
-            qpos_qvel_previous_action,
-            ang_vel,
-            velocity,
-            projected_gravity,
-        ])
-
-        observation = np.clip(observation, -10.0, 10.0)
-        return observation.astype(np.float32)
-
-    def sync(self):
-        ''' Run a single simulation step '''
-        self._safe_receive_world_update(retries=1)
-        self.Player.robot.commit_motor_targets_pd()
-        self.Player.server.send()
-        if self._target_dt > 0.0:
-            now = time.time()
-            if self._last_sync_time is None:
-                self._last_sync_time = now
-                return
-            elapsed = now - self._last_sync_time
-            remaining = self._target_dt - elapsed
-            if remaining > 0.0:
-                time.sleep(remaining)
-                now = time.time()
-            self._last_sync_time = now
-
-    def debug_joint_status(self):
-        robot = self.Player.robot
-        actual_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        target_joint_positions = getattr(
-            self,
-            'target_joint_positions',
-            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
-        )
-        joint_error = actual_joint_positions - target_joint_positions
-        leg_slice = slice(11, None)
-
-        self.debug_log(
-            "[WalkDebug] "
-            f"step={self.step_counter} "
-            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
-            f"target_xy={np.round(self.target_position, 3).tolist()} "
-            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
-            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
-            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
-            f"fallen={self.Player.world.global_position[2] < 0.3}"
-        )
-        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
-
-    def reset(self, seed=None, options=None):
-        '''
-        Reset and stabilize the robot
-        Note: for some behaviors it would be better to reduce stabilization or add noise
-        '''
-        r = self.Player.robot
-        super().reset(seed=seed)
-        if seed is not None:
-            np.random.seed(seed)
-
-        target_distance = np.random.uniform(self.reset_target_distance_min, self.reset_target_distance_max)
-        target_bearing_deg = np.random.uniform(-self.reset_target_bearing_range_deg, self.reset_target_bearing_range_deg)
-
-        self.step_counter = 0
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
-        self.last_yaw_error = None
-        self.walk_cycle_step = 0
-        self._reward_debug_steps_left = 0
-
-        # 随机 beam 目标位置和朝向，增加训练多样性
-        beam_x = (random() - 0.5) * 10
-        beam_y = (random() - 0.5) * 10
-        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
-
-        for _ in range(5):
-            self._safe_receive_world_update(retries=2)
-            self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
-            self.Player.server.send()
-
-        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
-        finished_count = 0
-        for _ in range(50):
-            finished = self.Player.skills_manager.execute("Neutral")
-            self.sync()
-            if finished:
-                finished_count += 1
-                if finished_count >= 20:  # 假设需要连续20次完成才算成功
-                    break
-
-        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-            # Perturb waist + lower body only (10:), keep head/arms stable.
-            perturb_action[10:] = np.random.uniform(
-                -self.reset_joint_noise_rad,
-                self.reset_joint_noise_rad,
-                size=(self.no_of_actions - 10,)
-            )
-
-            for _ in range(self.reset_perturb_steps):
-                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-            for i in range(self.reset_recover_steps):
-                # Linearly fade perturbation to help policy start from near-neutral.
-                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-        # memory variables
-        self.sync()  
-        self.initial_position = np.array(self.Player.world.global_position[:2])
-        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions, np.float32)
-        # Randomize global target bearing so policy must learn to rotate toward it first.
-        heading_deg = float(r.global_orientation_euler[2])
-        target_offset = MathOps.rotate_2d_vec(
-            np.array([target_distance, 0.0]),
-            heading_deg + target_bearing_deg,
-            is_rad=False,
-        )
-        point1 = self.initial_position + target_offset
-        self.point_list = [point1]
-        self.target_position = self.point_list[self.waypoint_index]
-        self.initial_height = self.Player.world.global_position[2]
-
-        return self.observe(True), {}
-
-    def render(self, mode='human', close=False):
-        return
-
-    
-    def compute_reward(self, previous_pos, current_pos, action):
-        height = float(self.Player.world.global_position[2])
-        robot = self.Player.robot
-
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-        tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        ang_vel = np.deg2rad(robot.gyroscope)
-        rp_ang_vel_mag = float(np.linalg.norm(ang_vel[:2]))
-
-        is_fallen = height < 0.55
-        if is_fallen:
-            # remain = max(0, 800 - self.step_counter)
-            # return -8.0 - 0.01 * remain
-            return -1.0
-        
-
-        if np.linalg.norm(current_pos - previous_pos) > 0.005:
-            position_penalty = -0.1 * float(np.linalg.norm(current_pos - previous_pos))
-        else:
-            position_penalty = 0.0
-
-
-        # Turn-to-target shaping.
-        to_target = self.target_position - current_pos
-        dist_to_target = float(np.linalg.norm(to_target))
-        if dist_to_target > 1e-6:
-            target_yaw = math.atan2(float(to_target[1]), float(to_target[0]))
-        else:
-            target_yaw = 0.0
-
-        robot_yaw = math.radians(float(robot.global_orientation_euler[2]))
-        yaw_error = target_yaw - robot_yaw
-
-        # Main heading objective: face the target direction.
-        # heading_align_reward = 1.0 * math.cos(yaw_error)
-
-        abs_yaw_error = abs(yaw_error)
-        alive_bonus = 2.0 * max(0.0, 1.0 - abs_yaw_error / math.pi)
-
-        if self.last_yaw_error is None:
-            heading_progress_reward = 0.0
-        else:
-            prev_abs_yaw_error = abs(self.last_yaw_error)
-            yaw_err_delta = prev_abs_yaw_error - abs_yaw_error
-            progress_gate = 1.0 if abs_yaw_error > math.radians(4.0) else 0.0
-            heading_progress_reward =  progress_gate * yaw_err_delta
-            heading_progress_reward = float(np.clip(heading_progress_reward, -0.7, 0.7))
-        self.last_yaw_error = yaw_error
-
-        # action_penalty = -0.01 * float(np.linalg.norm(action))
-        smoothness_penalty = -0.02 * float(np.linalg.norm(action - self.last_action_for_reward))
-
-        posture_penalty = -0.6 * tilt_mag
-        # Penalize roll/pitch rotational shake but do not penalize yaw turning directly.
-        ang_vel_penalty = -0.06 * rp_ang_vel_mag
-
-        joint_pos = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        ) * self.train_sim_flip
-        left_hip_roll = float(joint_pos[12])
-        right_hip_roll = float(joint_pos[18])
-
-        left_ankle_roll = float(joint_pos[16])
-        right_ankle_roll = float(joint_pos[22])
-
-        max_leg_roll = 0.75  # 防止劈叉姿势
-        split_penalty = -0.8 * max(0.0, (-left_hip_roll + right_hip_roll - 2 * max_leg_roll) / max_leg_roll)
-        left_hip_yaw = float(joint_pos[13])
-        right_hip_yaw = float(joint_pos[19])
-
-        min_leg_separation = 0.1  # 最小腿间距（防止贴得太近）
-        # 惩罚腿过分靠拢（内收）- 基于两腿间距
-        leg_separation = -left_hip_roll + right_hip_roll
-        inward_penalty = -0.25 * max(0.0, (min_leg_separation - leg_separation) / min_leg_separation)
-        
-        
-        # 脚踝roll角度检测：防止过度外翻或内翻
-        max_ankle_roll = 0.35  # 最大允许的脚踝roll角度
-        
-        # 惩罚脚踝过度外翻/内翻（绝对值过大）
-        ankle_roll_penalty = -0.5 * max(0.0, (abs(left_ankle_roll) + abs(right_ankle_roll) - 2 * max_ankle_roll) / max_ankle_roll)
-        
-        # 惩罚两脚踝roll方向相反（不稳定姿势）
-        ankle_roll_cross_penalty = -0.3 * max(0.0, -(left_ankle_roll * right_ankle_roll))
-
-       # 分别惩罚左右大腿过度转动
-        max_hip_yaw = 1  # 最大允许的yaw角度
-        left_hip_yaw_penalty = -0.4 * max(0.0, abs(left_hip_yaw) - max_hip_yaw)
-        right_hip_yaw_penalty = -0.4 * max(0.0, abs(right_hip_yaw) - max_hip_yaw)
-        # 智能交叉腿惩罚：只在站立时惩罚，转身时允许交叉腿
-        yaw_rate = float(np.deg2rad(robot.gyroscope[2]))
-        yaw_rate_abs = abs(yaw_rate)
-
-        # 当转身速度较小时才惩罚交叉腿（站立状态）
-        cross_leg_gate = max(0.0, 1.0 - yaw_rate_abs / math.radians(8.0))
-        hip_yaw_cross_penalty = -1.0 * cross_leg_gate * max(0.0, -(left_hip_yaw * right_hip_yaw)) if left_hip_yaw > 0 and right_hip_yaw < 0 else 0.0
-
-
-        target_height = self.initial_height
-        height_error =  height - target_height
-        height_error = height - target_height
-        
-        height_penalty = -(math.exp(12*abs(height_error))-1) if height_error > 0.04 else 0
-
-        # # 在 compute_reward 开头附近，添加高度变化率计算
-        # if not hasattr(self, 'last_height'):
-        #     self.last_height = height
-        #     self.last_height_time = self.step_counter  # 可选，用于时间间隔
-        # height_rate = height - self.last_height  # 正为上升，负为下降
-        # self.last_height = height
-
-        # 惩罚高度下降（负变化率）
-        # height_down_penalty = -5.0 * max(0, -height_rate)  # 系数可调，-height_rate 为正表示下降幅度
-
-        # # 在 compute_reward 中
-        # if self.step_counter > 50:
-        #     avg_prev_action = np.mean(self.prev_action_history, axis=0)
-        #     novelty = float(np.linalg.norm(action - avg_prev_action))
-        #     exploration_bonus = 0.05 * novelty
-        # else:
-        #     exploration_bonus = 0
-
-        # self.prev_action_history[self.history_idx] = action
-        # self.history_idx = (self.history_idx + 1) % 50
-
-
-        total = (
-            # progress_reward  + 
-                alive_bonus + 
-                heading_progress_reward +
-                # lateral_penalty +
-                # action_penalty +
-                smoothness_penalty +
-                posture_penalty
-                + ang_vel_penalty
-                + height_penalty
-                + ankle_roll_penalty
-                + ankle_roll_cross_penalty
-                + split_penalty
-                + inward_penalty
-                # + leg_proximity_penalty
-                + left_hip_yaw_penalty
-                + right_hip_yaw_penalty
-                + hip_yaw_cross_penalty
-                + position_penalty
-                # + stance_collapse_penalty
-                # + hip_yaw_yaw_cross_penalty
-                # + stance_collapse_penalty
-                #  + cross_leg_penalty
-                #  + exploration_bonus
-                # + height_down_penalty
-                 )
-        # print(height_error, height_penalty)
-        
-        now = time.time()
-        if self.reward_debug_interval_sec > 0 and now - self._reward_debug_last_time >= self.reward_debug_interval_sec:
-            self._reward_debug_last_time = now
-            self._reward_debug_steps_left = max(1, self.reward_debug_burst_steps)
-
-        if self._reward_debug_steps_left > 0:
-            self._reward_debug_steps_left -= 1
-            self.debug_log(
-                f"height_penalty:{height_penalty:.4f},"
-                f"smoothness_penalty:{smoothness_penalty:.4f},"
-                f"posture_penalty:{posture_penalty:.4f},"
-                f"heading_progress_reward:{heading_progress_reward:.4f},"
-                # f"stance_collapse_penalty:{stance_collapse_penalty:.4f},"
-                # f"cross_leg_penalty:{cross_leg_penalty:.4f},"
-                f"ang_vel_penalty:{ang_vel_penalty:.4f},"
-                f"split_penalty:{split_penalty:.4f},"
-                f"ankle_roll_penalty:{ankle_roll_penalty:.4f},"
-                f"ankle_roll_cross_penalty:{ankle_roll_cross_penalty:.4f},"
-                f"left_hip_yaw_penalty:{left_hip_yaw_penalty:.4f},"
-                f"right_hip_yaw_penalty:{right_hip_yaw_penalty:.4f},"
-                f"hip_yaw_cross_penalty:{hip_yaw_cross_penalty:.4f},"
-                f"inward_penalty:{inward_penalty:.4f},"
-                f"position_penalty:{position_penalty:.4f},"
-                # f"leg_proximity_penalty:{leg_proximity_penalty:.4f},"
-
-                # f"stance_collapse_penalty:{stance_collapse_penalty:.4f},"
-                # f"hip_yaw_yaw_cross_penalty:{hip_yaw_yaw_cross_penalty:.4f},"
-                #   f"height_down_penalty:{height_down_penalty:.4f}",
-                #   f"exploration_bonus:{exploration_bonus:.4f}"
-                f"alive_bonus:{alive_bonus:.4f},"
-                f"abs_yaw_error:{abs_yaw_error:.4f}"
-                f"total:{total:.4f}"
-                )
-
-        return total
-
-
-
-    def step(self, action):
-
-        r = self.Player.robot
-        max_action_delta =  0.5# Limit how much the action can change from the previous step to encourage smoother motions.
-        if self.previous_action is not None:
-            action = np.clip(action, self.previous_action - max_action_delta, self.previous_action + max_action_delta)
-        action[0:2] = 0
-        action[3] = 4
-        action[7] = -4
-        action[2] = 0
-        action[6] = 0
-        action[4] = 0
-        action[5] = -5
-        action[8] = 0
-        action[9] = 5
-        # action[12] = -1.0
-        # action[18] = 1.0
-        # action[13] = -1.0
-        # action[19] = 1.0
-        self.previous_action = action.copy()
-
-        self.target_joint_positions = (
-                # self.joint_nominal_position +
-                 self.scaling_factor * action
-        )
-        self.target_joint_positions *= self.train_sim_flip
-
-        for idx, target in enumerate(self.target_joint_positions):
-            r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=150, kd=40
-            )
-
-        self.previous_action = action.copy()
-
-        self.sync()  # run simulation step
-        self.step_counter += 1
-
-        if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
-            self.debug_joint_status()
-
-        current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
-
-        if self.step_counter % 10 == 0:
-            self.previous_pos = current_pos.copy()
-
-        # Compute reward based on movement from previous step
-        reward = self.compute_reward(self.previous_pos, current_pos, action)
-
-        self.last_action_for_reward = action.copy()
-
-        # Fall detection and penalty
-        is_fallen = self.Player.world.global_position[2] < 0.55
-
-        # terminal state: the robot is falling or timeout
-        terminated = is_fallen or self.step_counter > 800 or self.route_completed
-        truncated = False
-
-        return self.observe(), reward, terminated, truncated, {}
-
-
-class Train(Train_Base):
-    def __init__(self, script) -> None:
-        super().__init__(script)
-
-    def train(self, args):
-
-        # --------------------------------------- Learning parameters
-        n_envs = int(os.environ.get("GYM_CPU_N_ENVS", "20"))
-        if n_envs < 1:
-            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
-        server_warmup_sec = float(os.environ.get("GYM_CPU_SERVER_WARMUP_SEC", "3.0"))
-        n_steps_per_env = int(os.environ.get("GYM_CPU_TRAIN_STEPS_PER_ENV", "512"))  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = int(os.environ.get("GYM_CPU_TRAIN_BATCH_SIZE", "512"))  # should be a factor of (n_steps_per_env * n_envs)
-        total_steps = 30000000
-        learning_rate = float(os.environ.get("GYM_CPU_TRAIN_LR", "3e-4"))
-        folder_name = f'Turn_R{self.robot_type}'
-        model_path = f'./scripts/gyms/logs/{folder_name}/'
-
-        print(f"Model path: {model_path}")
-        print(f"Using {n_envs} parallel environments")
-
-        # --------------------------------------- Run algorithm
-        def init_env(i_env, monitor=False):
-            def thunk():
-                env = WalkEnv(self.ip, self.server_p + i_env)
-                if monitor:
-                    env = Monitor(env)
-                return env
-
-            return thunk
-
-        server_log_dir = os.path.join(model_path, "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1, no_render=True, no_realtime=True)  # include 1 extra server for testing
-
-        # Wait for servers to start
-        print(f"Starting {n_envs + 1} rcssservermj servers...")
-        if server_warmup_sec > 0:
-            print(f"Waiting {server_warmup_sec:.1f}s for server warmup...")
-            sleep(server_warmup_sec)
-        print("Servers started, creating environments...")
-
-        env = SubprocVecEnv([init_env(i, monitor=True) for i in range(n_envs)], start_method="spawn")
-        # Use single-process eval env to avoid extra subprocess fragility during callback evaluation.
-        eval_env = DummyVecEnv([init_env(n_envs, monitor=True)])
-
-        try:
-            # Custom policy network architecture
-            policy_kwargs = dict(
-                net_arch=dict(
-                    pi=[512, 256, 128],  # Policy network: 3 layers
-                    vf=[512, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
-            )
-
-            if "model_file" in args:  # retrain
-                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
-                                 batch_size=minibatch_size, learning_rate=learning_rate)
-            else:  # train new model
-                model = PPO(
-                    "MlpPolicy",
-                    env=env,
-                    verbose=1,
-                    n_steps=n_steps_per_env,
-                    batch_size=minibatch_size,
-                    learning_rate=learning_rate,
-                    device="cpu",
-                    policy_kwargs=policy_kwargs,
-                    ent_coef=float(os.environ.get("GYM_CPU_TRAIN_ENT_COEF", "0.05")),  # Entropy coefficient for exploration
-                    clip_range=float(os.environ.get("GYM_CPU_TRAIN_CLIP_RANGE", "0.2")),  # PPO clipping parameter
-                    gae_lambda=0.95,  # GAE lambda
-                    gamma=float(os.environ.get("GYM_CPU_TRAIN_GAMMA", "0.95")), # Discount factor
-                    # target_kl=0.03,
-                    n_epochs=int(os.environ.get("GYM_CPU_TRAIN_EPOCHS", "5")),
-                    tensorboard_log=f"./scripts/gyms/logs/{folder_name}/tensorboard/"
-                )
-
-            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
-                                          eval_freq=n_steps_per_env * 20, save_freq=n_steps_per_env * 20, eval_eps=7,
-                                          backup_env_file=__file__)
-        except KeyboardInterrupt:
-            sleep(1)  # wait for child processes
-            print("\nctrl+c pressed, aborting...\n")
-            servers.kill()
-            return
-
-        env.close()
-        eval_env.close()
-        servers.kill()
-
-    def test(self, args):
-
-        # Uses different server and monitor ports
-        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        test_no_render = os.environ.get("GYM_CPU_TEST_NO_RENDER", "0") == "1"
-        test_no_realtime = os.environ.get("GYM_CPU_TEST_NO_REALTIME", "0") == "1"
-
-        server = Train_Server(
-            self.server_p - 1,
-            self.monitor_p,
-            1,
-            no_render=test_no_render,
-            no_realtime=test_no_realtime,
-        )
-        env = WalkEnv(self.ip, self.server_p - 1)
-        model = PPO.load(args["model_file"], env=env)
-
-        try:
-            self.export_model(args["model_file"], args["model_file"] + ".pkl",
-                              False)  # Export to pkl to create custom behavior
-            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
-        except KeyboardInterrupt:
-            print()
-
-        env.close()
-        server.kill()
-
-
-if __name__ == "__main__":
-    from types import SimpleNamespace
-
-    # 创建默认参数
-    script_args = SimpleNamespace(
-        args=SimpleNamespace(
-            i='127.0.0.1',  # Server IP
-            p=3100,  # Server port
-            m=3200,  # Monitor port
-            r=0,  # Robot type
-            t='Gym',  # Team name
-            u=1  # Uniform number
-        )
-    )
-
-    trainer = Train(script_args)
-
-    run_mode = os.environ.get("GYM_CPU_MODE", "train").strip().lower()
-
-    if run_mode == "test":
-        test_model_file = os.environ.get("GYM_CPU_TEST_MODEL", "scripts/gyms/logs/Turn_R0_004/best_model.zip")
-        test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Turn_R0_004/")
-        trainer.test({"model_file": test_model_file, "folder_dir": test_folder})
-    else:
-        retrain_model = os.environ.get("GYM_CPU_TRAIN_MODEL", "").strip()
-        if retrain_model:
-            trainer.train({"model_file": retrain_model})
-        else:
-            trainer.train({})

scripts/gyms/logs/Turn_R0_007/Walk.py

@@ -1,823 +0,0 @@
-import os
-import numpy as np
-import math
-import time
-from time import sleep
-from random import random
-from random import uniform
-from itertools import count
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.monitor import Monitor
-from stable_baselines3.common.vec_env import SubprocVecEnv, DummyVecEnv
-
-import gymnasium as gym
-from gymnasium import spaces
-
-from scripts.commons.Train_Base import Train_Base
-from scripts.commons.Server import Server as Train_Server
-
-from agent.base_agent import Base_Agent
-from utils.math_ops import MathOps
-
-from scipy.spatial.transform import Rotation as R
-
-'''
-Objective:
-Learn how to run forward using step primitive
-----------
-- class Basic_Run: implements an OpenAI custom gym
-- class Train:  implements algorithms to train a new model or test an existing model
-'''
-
-
-class WalkEnv(gym.Env):
-    def __init__(self, ip, server_p) -> None:
-
-        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
-        self.Player = player = Base_Agent(
-            team_name="Gym",
-            number=1,
-            host=ip,
-            port=server_p
-        )
-        self.robot_type = self.Player.robot
-        self.step_counter = 0  # to limit episode size
-        self.force_play_on = True
-
-        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
-        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
-        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
-        self.isfallen = False
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.debug_every_n_steps = 5
-        self.enable_debug_joint_status = False
-        self.reward_debug_interval_sec = float(os.environ.get("GYM_CPU_REWARD_DEBUG_INTERVAL_SEC", "600"))
-        self.reward_debug_burst_steps = int(os.environ.get("GYM_CPU_REWARD_DEBUG_BURST_STEPS", "10"))
-        self._reward_debug_last_time = time.time()
-        self._reward_debug_steps_left = 0
-        self.calibrate_nominal_from_neutral = True
-        self.auto_calibrate_train_sim_flip = True
-        self.nominal_calibrated_once = False
-        self.flip_calibrated_once = False
-        self._target_hz = 0.0
-        self._target_dt = 0.0
-        self._last_sync_time = None
-        target_hz_env = 0
-        if target_hz_env:
-            try:
-                self._target_hz = float(target_hz_env)
-            except ValueError:
-                self._target_hz = 0.0
-        if self._target_hz > 0.0:
-            self._target_dt = 1.0 / self._target_hz
-
-        # State space
-        # 原始观测大小: 78
-        obs_size = 78
-        self.obs = np.zeros(obs_size, np.float32)
-        self.observation_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(obs_size,),
-            dtype=np.float32
-        )
-
-        action_dim = len(self.Player.robot.ROBOT_MOTORS)
-        self.no_of_actions = action_dim
-        self.action_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(action_dim,),
-            dtype=np.float32
-        )
-
-        # 中立姿态
-        self.joint_nominal_position = np.array(
-            [
-                0.0,  # 0: Head_yaw (he1)
-                0.0,  # 1: Head_pitch (he2)
-                0.0,  # 2: Left_Shoulder_Pitch (lae1)
-                0.0,  # 3: Left_Shoulder_Roll (lae2)
-                0.0,  # 4: Left_Elbow_Pitch (lae3)
-                0.0,  # 5: Left_Elbow_Yaw (lae4)
-                0.0,  # 6: Right_Shoulder_Pitch (rae1)
-                0.0,  # 7: Right_Shoulder_Roll (rae2)
-                0.0,  # 8: Right_Elbow_Pitch (rae3)
-                0.0,  # 9: Right_Elbow_Yaw (rae4)
-                0.0,  # 10: Waist (te1)
-                0.0,  # 11: Left_Hip_Pitch (lle1)
-                0.0,  # 12: Left_Hip_Roll (lle2)
-                1.0,  # 13: Left_Hip_Yaw (lle3)
-                0.0,  # 14: Left_Knee_Pitch (lle4)
-                0.0,  # 15: Left_Ankle_Pitch (lle5)
-                0.0,  # 16: Left_Ankle_Roll (lle6)
-                0.0,  # 17: Right_Hip_Pitch (rle1)
-                0.0,  # 18: Right_Hip_Roll (rle2)
-                1.0,  # 19: Right_Hip_Yaw (rle3)
-                0.0,  # 20: Right_Knee_Pitch (rle4)
-                0.0,  # 21: Right_Ankle_Pitch (rle5)
-                0.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-        self.joint_nominal_position = np.zeros(self.no_of_actions)
-        self.train_sim_flip = np.array(
-            [
-                1.0,  # 0: Head_yaw (he1)
-                -1.0,  # 1: Head_pitch (he2)
-                1.0,  # 2: Left_Shoulder_Pitch (lae1)
-                -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                -1.0,  # 4: Left_Elbow_Pitch (lae3)
-                1.0,  # 5: Left_Elbow_Yaw (lae4)
-                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                -1.0,  # 7: Right_Shoulder_Roll (rae2)
-                1.0,  # 8: Right_Elbow_Pitch (rae3)
-                1.0,  # 9: Right_Elbow_Yaw (rae4)
-                1.0,  # 10: Waist (te1)
-                1.0,  # 11: Left_Hip_Pitch (lle1)
-                -1.0,  # 12: Left_Hip_Roll (lle2)
-                -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,  # 14: Left_Knee_Pitch (lle4)
-                1.0,  # 15: Left_Ankle_Pitch (lle5)
-                -1.0,  # 16: Left_Ankle_Roll (lle6)
-                -1.0,  # 17: Right_Hip_Pitch (rle1)
-                -1.0,  # 18: Right_Hip_Roll (rle2)
-                -1.0,  # 19: Right_Hip_Yaw (rle3)
-                -1.0,  # 20: Right_Knee_Pitch (rle4)
-                -1.0,  # 21: Right_Ankle_Pitch (rle5)
-                -1.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-
-        self.scaling_factor = 0.3
-        # self.scaling_factor = 1
-
-        # Encourage a minimum lateral stance so the policy avoids feet overlap.
-        self.min_stance_rad = 0.10
-
-        # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = False
-        self.reset_beam_yaw_range_deg = float(os.environ.get("GYM_CPU_RESET_BEAM_YAW_RANGE_DEG", "180"))
-        self.reset_target_bearing_range_deg = float(os.environ.get("GYM_CPU_RESET_TARGET_BEARING_RANGE_DEG", "45"))
-        self.reset_target_distance_min = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MIN", "1.2"))
-        self.reset_target_distance_max = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MAX", "2.8"))
-        if self.reset_target_distance_min > self.reset_target_distance_max:
-            self.reset_target_distance_min, self.reset_target_distance_max = (
-                self.reset_target_distance_max,
-                self.reset_target_distance_min,
-            )
-        self.reset_joint_noise_rad = 0.025
-        self.reset_perturb_steps = 4
-        self.reset_recover_steps = 8
-
-        self.reward_smoothness_scale = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_SCALE", "0.06"))
-        self.reward_smoothness_cap = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_CAP", "0.45"))
-        self.reward_head_toward_bonus = float(os.environ.get("GYM_CPU_REWARD_HEAD_TOWARD_BONUS", "1"))
-
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Track previous position
-        self.last_yaw_error = None
-        self.Player.server.connect()
-        # sleep(2.0)  # Longer wait for connection to establish completely
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-        self.start_time = time.time()
-
-    def _reconnect_server(self):
-        try:
-            self.Player.server.shutdown()
-        except Exception:
-            pass
-
-        self.Player.server.connect()
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-
-    def _safe_receive_world_update(self, retries=1):
-        last_exc = None
-        for attempt in range(retries + 1):
-            try:
-                self.Player.server.receive()
-                self.Player.world.update()
-                return
-            except (ConnectionResetError, OSError) as exc:
-                last_exc = exc
-                if attempt >= retries:
-                    raise
-                self._reconnect_server()
-        if last_exc is not None:
-            raise last_exc
-
-    def debug_log(self, message):
-        print(message)
-        try:
-            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
-            with open(log_path, "a", encoding="utf-8") as f:
-                f.write(message + "\n")
-        except OSError:
-            pass
-
-    @staticmethod
-    def _wrap_to_pi(angle_rad: float) -> float:
-        return (angle_rad + math.pi) % (2.0 * math.pi) - math.pi
-
-    def observe(self, init=False):
-
-        """获取当前观测值"""
-        robot = self.Player.robot
-        world = self.Player.world
-
-        # Safety check: ensure data is available
-
-        # 计算目标速度
-        raw_target = self.target_position - world.global_position[:2]
-        velocity = MathOps.rotate_2d_vec(
-            raw_target,
-            -robot.global_orientation_euler[2],
-            is_rad=False
-        )
-
-        # 计算相对方向
-        rel_orientation = MathOps.vector_angle(velocity) * 0.3
-        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
-
-        velocity = np.concatenate([velocity, np.array([rel_orientation])])
-        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
-        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
-
-        # 关节状态
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        qpos_qvel_previous_action = np.concatenate([
-            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
-            radian_joint_speeds / 110.0 * self.train_sim_flip,
-            self.previous_action / 10.0,
-        ])
-
-        # 角速度
-        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
-
-        # 投影的重力方向
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-
-        # 组合观测
-        observation = np.concatenate([
-            qpos_qvel_previous_action,
-            ang_vel,
-            velocity,
-            projected_gravity,
-        ])
-
-        observation = np.clip(observation, -10.0, 10.0)
-        return observation.astype(np.float32)
-
-    def sync(self):
-        ''' Run a single simulation step '''
-        self._safe_receive_world_update(retries=1)
-        self.Player.robot.commit_motor_targets_pd()
-        self.Player.server.send()
-        if self._target_dt > 0.0:
-            now = time.time()
-            if self._last_sync_time is None:
-                self._last_sync_time = now
-                return
-            elapsed = now - self._last_sync_time
-            remaining = self._target_dt - elapsed
-            if remaining > 0.0:
-                time.sleep(remaining)
-                now = time.time()
-            self._last_sync_time = now
-
-    def debug_joint_status(self):
-        robot = self.Player.robot
-        actual_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        target_joint_positions = getattr(
-            self,
-            'target_joint_positions',
-            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
-        )
-        joint_error = actual_joint_positions - target_joint_positions
-        leg_slice = slice(11, None)
-
-        self.debug_log(
-            "[WalkDebug] "
-            f"step={self.step_counter} "
-            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
-            f"target_xy={np.round(self.target_position, 3).tolist()} "
-            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
-            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
-            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
-            f"fallen={self.Player.world.global_position[2] < 0.3}"
-        )
-        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
-
-    def reset(self, seed=None, options=None):
-        '''
-        Reset and stabilize the robot
-        Note: for some behaviors it would be better to reduce stabilization or add noise
-        '''
-        r = self.Player.robot
-        super().reset(seed=seed)
-        if seed is not None:
-            np.random.seed(seed)
-
-        target_distance = np.random.uniform(self.reset_target_distance_min, self.reset_target_distance_max)
-        target_bearing_deg = np.random.uniform(-self.reset_target_bearing_range_deg, self.reset_target_bearing_range_deg)
-
-        self.step_counter = 0
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
-        self.last_yaw_error = None
-        self.walk_cycle_step = 0
-        self._reward_debug_steps_left = 0
-
-        # 随机 beam 目标位置和朝向，增加训练多样性
-        beam_x = (random() - 0.5) * 10
-        beam_y = (random() - 0.5) * 10
-        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
-
-        for _ in range(5):
-            self._safe_receive_world_update(retries=2)
-            self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
-            self.Player.server.send()
-
-        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
-        finished_count = 0
-        for _ in range(50):
-            finished = self.Player.skills_manager.execute("Neutral")
-            self.sync()
-            if finished:
-                finished_count += 1
-                if finished_count >= 20:  # 假设需要连续20次完成才算成功
-                    break
-
-        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-            # Perturb waist + lower body only (10:), keep head/arms stable.
-            perturb_action[10:] = np.random.uniform(
-                -self.reset_joint_noise_rad,
-                self.reset_joint_noise_rad,
-                size=(self.no_of_actions - 10,)
-            )
-
-            for _ in range(self.reset_perturb_steps):
-                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-            for i in range(self.reset_recover_steps):
-                # Linearly fade perturbation to help policy start from near-neutral.
-                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-        # memory variables
-        self.sync()  
-        self.initial_position = np.array(self.Player.world.global_position[:2])
-        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions, np.float32)
-        # Randomize global target bearing so policy must learn to rotate toward it first.
-        heading_deg = float(r.global_orientation_euler[2])
-        target_offset = MathOps.rotate_2d_vec(
-            np.array([target_distance, 0.0]),
-            heading_deg + target_bearing_deg,
-            is_rad=False,
-        )
-        point1 = self.initial_position + target_offset
-        self.point_list = [point1]
-        self.target_position = self.point_list[self.waypoint_index]
-        self.initial_height = self.Player.world.global_position[2]
-
-        return self.observe(True), {}
-
-    def render(self, mode='human', close=False):
-        return
-
-    
-    def compute_reward(self, previous_pos, current_pos, action):
-        height = float(self.Player.world.global_position[2])
-        robot = self.Player.robot
-
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-        tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        ang_vel = np.deg2rad(robot.gyroscope)
-        rp_ang_vel_mag = float(np.linalg.norm(ang_vel[:2]))
-
-        is_fallen = height < 0.55
-        if is_fallen:
-            # remain = max(0, 800 - self.step_counter)
-            # return -8.0 - 0.01 * remain
-            return -20.0
-        
-
-        if np.linalg.norm(current_pos - previous_pos) > 0.005:
-            position_penalty = -float(np.linalg.norm(current_pos - previous_pos))
-        else:
-            position_penalty = 0.0
-
-
-        # Turn-to-target shaping.
-        to_target = self.target_position - current_pos
-        dist_to_target = float(np.linalg.norm(to_target))
-        if dist_to_target > 1e-6:
-            target_yaw = math.atan2(float(to_target[1]), float(to_target[0]))
-        else:
-            target_yaw = 0.0
-
-        robot_yaw = math.radians(float(robot.global_orientation_euler[2]))
-        yaw_error = target_yaw - robot_yaw
-
-        # Main heading objective: face the target direction.
-        # heading_align_reward = 1.0 * math.cos(yaw_error)
-
-        abs_yaw_error = abs(yaw_error)
-        alive_bonus = 2.0 * max(0.0, 1.0 - abs_yaw_error / math.pi)
-        head_toward_bonus = self.reward_head_toward_bonus if abs_yaw_error < math.radians(4.0) else 0.0
-
-        if self.last_yaw_error is None:
-            heading_progress_reward = 0.0
-        else:
-            prev_abs_yaw_error = abs(self.last_yaw_error)
-            yaw_err_delta = prev_abs_yaw_error - abs_yaw_error
-            progress_gate = 1.0 if abs_yaw_error > math.radians(4.0) else 0.0
-            heading_progress_reward =  0.8 * progress_gate * yaw_err_delta
-            heading_progress_reward = float(np.clip(heading_progress_reward, -0.4, 0.4))
-        self.last_yaw_error = yaw_error
-
-        # action_penalty = -0.01 * float(np.linalg.norm(action))
-        smoothness_penalty = -0.02 * float(np.linalg.norm(action - self.last_action_for_reward))
-
-        posture_penalty = -0.6 * tilt_mag
-        # Penalize roll/pitch rotational shake but do not penalize yaw turning directly.
-        ang_vel_penalty = -0.06 * rp_ang_vel_mag
-
-        joint_pos = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        ) * self.train_sim_flip
-        left_hip_roll = float(joint_pos[12])
-        right_hip_roll = float(joint_pos[18])
-
-        left_ankle_roll = float(joint_pos[16])
-        right_ankle_roll = float(joint_pos[22])
-
-        max_leg_roll = 0.75  # 防止劈叉姿势
-        split_penalty = -0.8 * max(0.0, (-left_hip_roll + right_hip_roll - 2 * max_leg_roll) / max_leg_roll)
-        left_hip_yaw = float(joint_pos[13])
-        right_hip_yaw = float(joint_pos[19])
-
-        min_leg_separation = 0.1  # 最小腿间距（防止贴得太近）
-        # 惩罚腿过分靠拢（内收）- 基于两腿间距
-        leg_separation = -left_hip_roll + right_hip_roll
-        inward_penalty = -0.25 * max(0.0, (min_leg_separation - leg_separation) / min_leg_separation)
-        
-        
-        # 脚踝roll角度检测：防止过度外翻或内翻
-        max_ankle_roll = 0.35  # 最大允许的脚踝roll角度
-        
-        # 惩罚脚踝过度外翻/内翻（绝对值过大）
-        ankle_roll_penalty = -0.5 * max(0.0, (abs(left_ankle_roll) + abs(right_ankle_roll) - 2 * max_ankle_roll) / max_ankle_roll)
-        
-        # 惩罚两脚踝roll方向相反（不稳定姿势）
-        ankle_roll_cross_penalty = -0.3 * max(0.0, -(left_ankle_roll * right_ankle_roll))
-
-       # 分别惩罚左右大腿过度转动
-        max_hip_yaw = 1  # 最大允许的yaw角度
-        left_hip_yaw_penalty = -0.4 * max(0.0, abs(left_hip_yaw) - max_hip_yaw)
-        right_hip_yaw_penalty = -0.4 * max(0.0, abs(right_hip_yaw) - max_hip_yaw)
-        # 智能交叉腿惩罚：只在站立时惩罚，转身时允许交叉腿
-        yaw_rate = float(np.deg2rad(robot.gyroscope[2]))
-        yaw_rate_abs = abs(yaw_rate)
-
-        # 当转身速度较小时才惩罚交叉腿（站立状态）
-        cross_leg_gate = max(0.0, 1.0 - yaw_rate_abs / math.radians(8.0))
-        hip_yaw_cross_penalty = -1.0 * cross_leg_gate * max(0.0, -(left_hip_yaw * right_hip_yaw)) if left_hip_yaw > 0 and right_hip_yaw < 0 else 0.0
-
-
-        target_height = self.initial_height
-        height_error =  height - target_height
-        height_error = height - target_height
-        
-        height_penalty = -(math.exp(12*abs(height_error))-1) if height_error > 0.04 else 0
-
-        # # 在 compute_reward 开头附近，添加高度变化率计算
-        # if not hasattr(self, 'last_height'):
-        #     self.last_height = height
-        #     self.last_height_time = self.step_counter  # 可选，用于时间间隔
-        # height_rate = height - self.last_height  # 正为上升，负为下降
-        # self.last_height = height
-
-        # 惩罚高度下降（负变化率）
-        # height_down_penalty = -5.0 * max(0, -height_rate)  # 系数可调，-height_rate 为正表示下降幅度
-
-        # # 在 compute_reward 中
-        # if self.step_counter > 50:
-        #     avg_prev_action = np.mean(self.prev_action_history, axis=0)
-        #     novelty = float(np.linalg.norm(action - avg_prev_action))
-        #     exploration_bonus = 0.05 * novelty
-        # else:
-        #     exploration_bonus = 0
-
-        # self.prev_action_history[self.history_idx] = action
-        # self.history_idx = (self.history_idx + 1) % 50
-
-
-        total = (
-            # progress_reward  + 
-                alive_bonus + 
-                head_toward_bonus +
-                heading_progress_reward +
-                # lateral_penalty +
-                # action_penalty +
-                smoothness_penalty +
-                posture_penalty
-                + ang_vel_penalty
-                + height_penalty
-                + ankle_roll_penalty
-                + ankle_roll_cross_penalty
-                + split_penalty
-                + inward_penalty
-                # + leg_proximity_penalty
-                + left_hip_yaw_penalty
-                + right_hip_yaw_penalty
-                + hip_yaw_cross_penalty
-                + position_penalty
-                # + stance_collapse_penalty
-                # + hip_yaw_yaw_cross_penalty
-                # + stance_collapse_penalty
-                #  + cross_leg_penalty
-                #  + exploration_bonus
-                # + height_down_penalty
-                 )
-        # print(height_error, height_penalty)
-        
-        now = time.time()
-        if self.reward_debug_interval_sec > 0 and now - self._reward_debug_last_time >= self.reward_debug_interval_sec:
-            self._reward_debug_last_time = now
-            self._reward_debug_steps_left = max(1, self.reward_debug_burst_steps)
-
-        if self._reward_debug_steps_left > 0:
-            self._reward_debug_steps_left -= 1
-            self.debug_log(
-                f"height_penalty:{height_penalty:.4f},"
-                f"smoothness_penalty:{smoothness_penalty:.4f},"
-                f"posture_penalty:{posture_penalty:.4f},"
-                f"heading_progress_reward:{heading_progress_reward:.4f},"
-                # f"stance_collapse_penalty:{stance_collapse_penalty:.4f},"
-                # f"cross_leg_penalty:{cross_leg_penalty:.4f},"
-                f"ang_vel_penalty:{ang_vel_penalty:.4f},"
-                f"split_penalty:{split_penalty:.4f},"
-                f"ankle_roll_penalty:{ankle_roll_penalty:.4f},"
-                f"ankle_roll_cross_penalty:{ankle_roll_cross_penalty:.4f},"
-                f"left_hip_yaw_penalty:{left_hip_yaw_penalty:.4f},"
-                f"right_hip_yaw_penalty:{right_hip_yaw_penalty:.4f},"
-                f"hip_yaw_cross_penalty:{hip_yaw_cross_penalty:.4f},"
-                f"inward_penalty:{inward_penalty:.4f},"
-                f"position_penalty:{position_penalty:.4f},"
-                # f"leg_proximity_penalty:{leg_proximity_penalty:.4f},"
-
-                # f"stance_collapse_penalty:{stance_collapse_penalty:.4f},"
-                # f"hip_yaw_yaw_cross_penalty:{hip_yaw_yaw_cross_penalty:.4f},"
-                #   f"height_down_penalty:{height_down_penalty:.4f}",
-                #   f"exploration_bonus:{exploration_bonus:.4f}"
-                f"alive_bonus:{alive_bonus:.4f},"
-                f"abs_yaw_error:{abs_yaw_error:.4f}"
-                f"total:{total:.4f}"
-                )
-
-        return total
-
-
-
-    def step(self, action):
-
-        r = self.Player.robot
-        max_action_delta =  0.5# Limit how much the action can change from the previous step to encourage smoother motions.
-        if self.previous_action is not None:
-            action = np.clip(action, self.previous_action - max_action_delta, self.previous_action + max_action_delta)
-        action[0:2] = 0
-        action[3] = 4
-        action[7] = -4
-        action[2] = 0
-        action[6] = 0
-        action[4] = 0
-        action[5] = -5
-        action[8] = 0
-        action[9] = 5
-        # action[12] = -1.0
-        # action[18] = 1.0
-        # action[13] = -1.0
-        # action[19] = 1.0
-        self.previous_action = action.copy()
-
-        self.target_joint_positions = (
-                # self.joint_nominal_position +
-                 self.scaling_factor * action
-        )
-        self.target_joint_positions *= self.train_sim_flip
-
-        for idx, target in enumerate(self.target_joint_positions):
-            r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=150, kd=40
-            )
-
-        self.previous_action = action.copy()
-
-        self.sync()  # run simulation step
-        self.step_counter += 1
-
-        if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
-            self.debug_joint_status()
-
-        current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
-
-        if self.step_counter % 10 == 0:
-            self.previous_pos = current_pos.copy()
-
-        # Compute reward based on movement from previous step
-        reward = self.compute_reward(self.previous_pos, current_pos, action)
-
-        self.last_action_for_reward = action.copy()
-
-        # Fall detection and penalty
-        is_fallen = self.Player.world.global_position[2] < 0.55
-
-        # terminal state: the robot is falling or timeout
-        terminated = is_fallen or self.step_counter > 800 or self.route_completed
-        truncated = False
-
-        return self.observe(), reward, terminated, truncated, {}
-
-
-class Train(Train_Base):
-    def __init__(self, script) -> None:
-        super().__init__(script)
-
-    def train(self, args):
-
-        # --------------------------------------- Learning parameters
-        n_envs = int(os.environ.get("GYM_CPU_N_ENVS", "20"))
-        if n_envs < 1:
-            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
-        server_warmup_sec = float(os.environ.get("GYM_CPU_SERVER_WARMUP_SEC", "3.0"))
-        n_steps_per_env = int(os.environ.get("GYM_CPU_TRAIN_STEPS_PER_ENV", "512"))  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = int(os.environ.get("GYM_CPU_TRAIN_BATCH_SIZE", "512"))  # should be a factor of (n_steps_per_env * n_envs)
-        total_steps = 30000000
-        learning_rate = float(os.environ.get("GYM_CPU_TRAIN_LR", "3e-4"))
-        folder_name = f'Turn_R{self.robot_type}'
-        model_path = f'./scripts/gyms/logs/{folder_name}/'
-
-        print(f"Model path: {model_path}")
-        print(f"Using {n_envs} parallel environments")
-
-        # --------------------------------------- Run algorithm
-        def init_env(i_env, monitor=False):
-            def thunk():
-                env = WalkEnv(self.ip, self.server_p + i_env)
-                if monitor:
-                    env = Monitor(env)
-                return env
-
-            return thunk
-
-        server_log_dir = os.path.join(model_path, "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1, no_render=True, no_realtime=True)  # include 1 extra server for testing
-
-        # Wait for servers to start
-        print(f"Starting {n_envs + 1} rcssservermj servers...")
-        if server_warmup_sec > 0:
-            print(f"Waiting {server_warmup_sec:.1f}s for server warmup...")
-            sleep(server_warmup_sec)
-        print("Servers started, creating environments...")
-
-        env = SubprocVecEnv([init_env(i, monitor=True) for i in range(n_envs)], start_method="spawn")
-        # Use single-process eval env to avoid extra subprocess fragility during callback evaluation.
-        eval_env = DummyVecEnv([init_env(n_envs, monitor=True)])
-
-        try:
-            # Custom policy network architecture
-            policy_kwargs = dict(
-                net_arch=dict(
-                    pi=[512, 256, 128],  # Policy network: 3 layers
-                    vf=[512, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
-            )
-
-            if "model_file" in args:  # retrain
-                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
-                                 batch_size=minibatch_size, learning_rate=learning_rate)
-            else:  # train new model
-                model = PPO(
-                    "MlpPolicy",
-                    env=env,
-                    verbose=1,
-                    n_steps=n_steps_per_env,
-                    batch_size=minibatch_size,
-                    learning_rate=learning_rate,
-                    device="cpu",
-                    policy_kwargs=policy_kwargs,
-                    ent_coef=float(os.environ.get("GYM_CPU_TRAIN_ENT_COEF", "0.05")),  # Entropy coefficient for exploration
-                    clip_range=float(os.environ.get("GYM_CPU_TRAIN_CLIP_RANGE", "0.2")),  # PPO clipping parameter
-                    gae_lambda=0.95,  # GAE lambda
-                    gamma=float(os.environ.get("GYM_CPU_TRAIN_GAMMA", "0.95")), # Discount factor
-                    # target_kl=0.03,
-                    n_epochs=int(os.environ.get("GYM_CPU_TRAIN_EPOCHS", "5")),
-                    tensorboard_log=f"./scripts/gyms/logs/{folder_name}/tensorboard/"
-                )
-
-            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
-                                          eval_freq=n_steps_per_env * 20, save_freq=n_steps_per_env * 20, eval_eps=7,
-                                          backup_env_file=__file__)
-        except KeyboardInterrupt:
-            sleep(1)  # wait for child processes
-            print("\nctrl+c pressed, aborting...\n")
-            servers.kill()
-            return
-
-        env.close()
-        eval_env.close()
-        servers.kill()
-
-    def test(self, args):
-
-        # Uses different server and monitor ports
-        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        test_no_render = os.environ.get("GYM_CPU_TEST_NO_RENDER", "0") == "1"
-        test_no_realtime = os.environ.get("GYM_CPU_TEST_NO_REALTIME", "0") == "1"
-
-        server = Train_Server(
-            self.server_p - 1,
-            self.monitor_p,
-            1,
-            no_render=test_no_render,
-            no_realtime=test_no_realtime,
-        )
-        env = WalkEnv(self.ip, self.server_p - 1)
-        model = PPO.load(args["model_file"], env=env)
-
-        try:
-            self.export_model(args["model_file"], args["model_file"] + ".pkl",
-                              False)  # Export to pkl to create custom behavior
-            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
-        except KeyboardInterrupt:
-            print()
-
-        env.close()
-        server.kill()
-
-
-if __name__ == "__main__":
-    from types import SimpleNamespace
-
-    # 创建默认参数
-    script_args = SimpleNamespace(
-        args=SimpleNamespace(
-            i='127.0.0.1',  # Server IP
-            p=3100,  # Server port
-            m=3200,  # Monitor port
-            r=0,  # Robot type
-            t='Gym',  # Team name
-            u=1  # Uniform number
-        )
-    )
-
-    trainer = Train(script_args)
-
-    run_mode = os.environ.get("GYM_CPU_MODE", "train").strip().lower()
-
-    if run_mode == "test":
-        test_model_file = os.environ.get("GYM_CPU_TEST_MODEL", "scripts/gyms/logs/Turn_R0_004/best_model.zip")
-        test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Turn_R0_004/")
-        trainer.test({"model_file": test_model_file, "folder_dir": test_folder})
-    else:
-        retrain_model = os.environ.get("GYM_CPU_TRAIN_MODEL", "").strip()
-        if retrain_model:
-            trainer.train({"model_file": retrain_model})
-        else:
-            trainer.train({})

scripts/gyms/logs/Turn_R0_008/Walk.py

@@ -1,849 +0,0 @@
-import os
-import numpy as np
-import math
-import time
-from time import sleep
-from random import random
-from random import uniform
-from itertools import count
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.monitor import Monitor
-from stable_baselines3.common.vec_env import SubprocVecEnv, DummyVecEnv
-
-import gymnasium as gym
-from gymnasium import spaces
-
-from scripts.commons.Train_Base import Train_Base
-from scripts.commons.Server import Server as Train_Server
-
-from agent.base_agent import Base_Agent
-from utils.math_ops import MathOps
-
-from scipy.spatial.transform import Rotation as R
-
-'''
-Objective:
-Learn how to run forward using step primitive
-----------
-- class Basic_Run: implements an OpenAI custom gym
-- class Train:  implements algorithms to train a new model or test an existing model
-'''
-
-
-class WalkEnv(gym.Env):
-    def __init__(self, ip, server_p) -> None:
-
-        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
-        self.Player = player = Base_Agent(
-            team_name="Gym",
-            number=1,
-            host=ip,
-            port=server_p
-        )
-        self.robot_type = self.Player.robot
-        self.step_counter = 0  # to limit episode size
-        self.force_play_on = True
-
-        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
-        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
-        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
-        self.isfallen = False
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.debug_every_n_steps = 5
-        self.enable_debug_joint_status = False
-        self.reward_debug_interval_sec = float(os.environ.get("GYM_CPU_REWARD_DEBUG_INTERVAL_SEC", "600"))
-        self.reward_debug_burst_steps = int(os.environ.get("GYM_CPU_REWARD_DEBUG_BURST_STEPS", "10"))
-        self._reward_debug_last_time = time.time()
-        self._reward_debug_steps_left = 0
-        self.calibrate_nominal_from_neutral = True
-        self.auto_calibrate_train_sim_flip = True
-        self.nominal_calibrated_once = False
-        self.flip_calibrated_once = False
-        self._target_hz = 0.0
-        self._target_dt = 0.0
-        self._last_sync_time = None
-        target_hz_env = 0
-        if target_hz_env:
-            try:
-                self._target_hz = float(target_hz_env)
-            except ValueError:
-                self._target_hz = 0.0
-        if self._target_hz > 0.0:
-            self._target_dt = 1.0 / self._target_hz
-
-        # State space
-        # 原始观测大小: 78
-        obs_size = 78
-        self.obs = np.zeros(obs_size, np.float32)
-        self.observation_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(obs_size,),
-            dtype=np.float32
-        )
-
-        action_dim = len(self.Player.robot.ROBOT_MOTORS)
-        self.no_of_actions = action_dim
-        self.action_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(action_dim,),
-            dtype=np.float32
-        )
-
-        # 中立姿态
-        self.joint_nominal_position = np.array(
-            [
-                0.0,  # 0: Head_yaw (he1)
-                0.0,  # 1: Head_pitch (he2)
-                0.0,  # 2: Left_Shoulder_Pitch (lae1)
-                0.0,  # 3: Left_Shoulder_Roll (lae2)
-                0.0,  # 4: Left_Elbow_Pitch (lae3)
-                0.0,  # 5: Left_Elbow_Yaw (lae4)
-                0.0,  # 6: Right_Shoulder_Pitch (rae1)
-                0.0,  # 7: Right_Shoulder_Roll (rae2)
-                0.0,  # 8: Right_Elbow_Pitch (rae3)
-                0.0,  # 9: Right_Elbow_Yaw (rae4)
-                0.0,  # 10: Waist (te1)
-                0.0,  # 11: Left_Hip_Pitch (lle1)
-                0.0,  # 12: Left_Hip_Roll (lle2)
-                1.0,  # 13: Left_Hip_Yaw (lle3)
-                0.0,  # 14: Left_Knee_Pitch (lle4)
-                0.0,  # 15: Left_Ankle_Pitch (lle5)
-                0.0,  # 16: Left_Ankle_Roll (lle6)
-                0.0,  # 17: Right_Hip_Pitch (rle1)
-                0.0,  # 18: Right_Hip_Roll (rle2)
-                1.0,  # 19: Right_Hip_Yaw (rle3)
-                0.0,  # 20: Right_Knee_Pitch (rle4)
-                0.0,  # 21: Right_Ankle_Pitch (rle5)
-                0.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-        self.joint_nominal_position = np.zeros(self.no_of_actions)
-        self.train_sim_flip = np.array(
-            [
-                1.0,  # 0: Head_yaw (he1)
-                -1.0,  # 1: Head_pitch (he2)
-                1.0,  # 2: Left_Shoulder_Pitch (lae1)
-                -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                -1.0,  # 4: Left_Elbow_Pitch (lae3)
-                1.0,  # 5: Left_Elbow_Yaw (lae4)
-                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                -1.0,  # 7: Right_Shoulder_Roll (rae2)
-                1.0,  # 8: Right_Elbow_Pitch (rae3)
-                1.0,  # 9: Right_Elbow_Yaw (rae4)
-                1.0,  # 10: Waist (te1)
-                1.0,  # 11: Left_Hip_Pitch (lle1)
-                -1.0,  # 12: Left_Hip_Roll (lle2)
-                -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,  # 14: Left_Knee_Pitch (lle4)
-                1.0,  # 15: Left_Ankle_Pitch (lle5)
-                -1.0,  # 16: Left_Ankle_Roll (lle6)
-                -1.0,  # 17: Right_Hip_Pitch (rle1)
-                -1.0,  # 18: Right_Hip_Roll (rle2)
-                -1.0,  # 19: Right_Hip_Yaw (rle3)
-                -1.0,  # 20: Right_Knee_Pitch (rle4)
-                -1.0,  # 21: Right_Ankle_Pitch (rle5)
-                -1.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-
-        self.scaling_factor = 0.3
-        # self.scaling_factor = 1
-
-        # Encourage a minimum lateral stance so the policy avoids feet overlap.
-        self.min_stance_rad = 0.10
-
-        # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = False
-        self.reset_beam_yaw_range_deg = float(os.environ.get("GYM_CPU_RESET_BEAM_YAW_RANGE_DEG", "180"))
-        self.reset_target_bearing_range_deg = float(os.environ.get("GYM_CPU_RESET_TARGET_BEARING_RANGE_DEG", "45"))
-        self.reset_target_distance_min = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MIN", "1.2"))
-        self.reset_target_distance_max = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MAX", "2.8"))
-        if self.reset_target_distance_min > self.reset_target_distance_max:
-            self.reset_target_distance_min, self.reset_target_distance_max = (
-                self.reset_target_distance_max,
-                self.reset_target_distance_min,
-            )
-        self.reset_joint_noise_rad = 0.025
-        self.reset_perturb_steps = 4
-        self.reset_recover_steps = 8
-
-        self.reward_smoothness_scale = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_SCALE", "0.06"))
-        self.reward_smoothness_cap = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_CAP", "0.45"))
-        self.reward_head_toward_bonus = float(os.environ.get("GYM_CPU_REWARD_HEAD_TOWARD_BONUS", "1"))
-
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Track previous position
-        self.last_yaw_error = None
-        self.Player.server.connect()
-        # sleep(2.0)  # Longer wait for connection to establish completely
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-        self.start_time = time.time()
-
-    def _reconnect_server(self):
-        try:
-            self.Player.server.shutdown()
-        except Exception:
-            pass
-
-        self.Player.server.connect()
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-
-    def _safe_receive_world_update(self, retries=1):
-        last_exc = None
-        for attempt in range(retries + 1):
-            try:
-                self.Player.server.receive()
-                self.Player.world.update()
-                return
-            except (ConnectionResetError, OSError) as exc:
-                last_exc = exc
-                if attempt >= retries:
-                    raise
-                self._reconnect_server()
-        if last_exc is not None:
-            raise last_exc
-
-    def debug_log(self, message):
-        print(message)
-        try:
-            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
-            with open(log_path, "a", encoding="utf-8") as f:
-                f.write(message + "\n")
-        except OSError:
-            pass
-
-    @staticmethod
-    def _wrap_to_pi(angle_rad: float) -> float:
-        return (angle_rad + math.pi) % (2.0 * math.pi) - math.pi
-
-    def observe(self, init=False):
-
-        """获取当前观测值"""
-        robot = self.Player.robot
-        world = self.Player.world
-
-        # Safety check: ensure data is available
-
-        # 计算目标速度
-        raw_target = self.target_position - world.global_position[:2]
-        velocity = MathOps.rotate_2d_vec(
-            raw_target,
-            -robot.global_orientation_euler[2],
-            is_rad=False
-        )
-
-        # 计算相对方向
-        rel_orientation = MathOps.vector_angle(velocity) * 0.3
-        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
-
-        velocity = np.concatenate([velocity, np.array([rel_orientation])])
-        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
-        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
-
-        # 关节状态
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        qpos_qvel_previous_action = np.concatenate([
-            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
-            radian_joint_speeds / 110.0 * self.train_sim_flip,
-            self.previous_action / 10.0,
-        ])
-
-        # 角速度
-        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
-
-        # 投影的重力方向
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-
-        # 组合观测
-        observation = np.concatenate([
-            qpos_qvel_previous_action,
-            ang_vel,
-            velocity,
-            projected_gravity,
-        ])
-
-        observation = np.clip(observation, -10.0, 10.0)
-        return observation.astype(np.float32)
-
-    def sync(self):
-        ''' Run a single simulation step '''
-        self._safe_receive_world_update(retries=1)
-        self.Player.robot.commit_motor_targets_pd()
-        self.Player.server.send()
-        if self._target_dt > 0.0:
-            now = time.time()
-            if self._last_sync_time is None:
-                self._last_sync_time = now
-                return
-            elapsed = now - self._last_sync_time
-            remaining = self._target_dt - elapsed
-            if remaining > 0.0:
-                time.sleep(remaining)
-                now = time.time()
-            self._last_sync_time = now
-
-    def debug_joint_status(self):
-        robot = self.Player.robot
-        actual_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        target_joint_positions = getattr(
-            self,
-            'target_joint_positions',
-            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
-        )
-        joint_error = actual_joint_positions - target_joint_positions
-        leg_slice = slice(11, None)
-
-        self.debug_log(
-            "[WalkDebug] "
-            f"step={self.step_counter} "
-            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
-            f"target_xy={np.round(self.target_position, 3).tolist()} "
-            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
-            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
-            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
-            f"fallen={self.Player.world.global_position[2] < 0.3}"
-        )
-        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
-
-    def reset(self, seed=None, options=None):
-        '''
-        Reset and stabilize the robot
-        Note: for some behaviors it would be better to reduce stabilization or add noise
-        '''
-        r = self.Player.robot
-        super().reset(seed=seed)
-        if seed is not None:
-            np.random.seed(seed)
-
-        target_distance = np.random.uniform(self.reset_target_distance_min, self.reset_target_distance_max)
-        target_bearing_deg = np.random.uniform(-self.reset_target_bearing_range_deg, self.reset_target_bearing_range_deg)
-
-        self.step_counter = 0
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
-        self.last_yaw_error = None
-        self.walk_cycle_step = 0
-        self._reward_debug_steps_left = 0
-
-        # 随机 beam 目标位置和朝向，增加训练多样性
-        beam_x = (random() - 0.5) * 10
-        beam_y = (random() - 0.5) * 10
-        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
-
-        for _ in range(5):
-            self._safe_receive_world_update(retries=2)
-            self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
-            self.Player.server.send()
-
-        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
-        finished_count = 0
-        for _ in range(50):
-            finished = self.Player.skills_manager.execute("Neutral")
-            self.sync()
-            if finished:
-                finished_count += 1
-                if finished_count >= 20:  # 假设需要连续20次完成才算成功
-                    break
-
-        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-            # Perturb waist + lower body only (10:), keep head/arms stable.
-            perturb_action[10:] = np.random.uniform(
-                -self.reset_joint_noise_rad,
-                self.reset_joint_noise_rad,
-                size=(self.no_of_actions - 10,)
-            )
-
-            for _ in range(self.reset_perturb_steps):
-                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-            for i in range(self.reset_recover_steps):
-                # Linearly fade perturbation to help policy start from near-neutral.
-                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-        # memory variables
-        self.sync()  
-        self.initial_position = np.array(self.Player.world.global_position[:2])
-        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions, np.float32)
-        # Randomize global target bearing so policy must learn to rotate toward it first.
-        heading_deg = float(r.global_orientation_euler[2])
-        target_offset = MathOps.rotate_2d_vec(
-            np.array([target_distance, 0.0]),
-            heading_deg + target_bearing_deg,
-            is_rad=False,
-        )
-        point1 = self.initial_position + target_offset
-        self.point_list = [point1]
-        self.target_position = self.point_list[self.waypoint_index]
-        self.initial_height = self.Player.world.global_position[2]
-
-        return self.observe(True), {}
-
-    def render(self, mode='human', close=False):
-        return
-
-    
-    def compute_reward(self, previous_pos, current_pos, action):
-        height = float(self.Player.world.global_position[2])
-        robot = self.Player.robot
-
-
-        joint_pos_rad = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        joint_speed_rad = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-        tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        ang_vel = np.deg2rad(robot.gyroscope)
-        rp_ang_vel_mag = float(np.linalg.norm(ang_vel[:2]))
-
-        is_fallen = height < 0.55
-        if is_fallen:
-            # remain = max(0, 800 - self.step_counter)
-            # return -8.0 - 0.01 * remain
-            return -20.0
-        
-
-        if np.linalg.norm(current_pos - previous_pos) > 0.005:
-            position_penalty = -3 * float(np.linalg.norm(current_pos - previous_pos))
-        else:
-            position_penalty = 0.0
-
-
-        # Turn-to-target shaping.
-        to_target = self.target_position - current_pos
-        dist_to_target = float(np.linalg.norm(to_target))
-        if dist_to_target > 1e-6:
-            target_yaw = math.atan2(float(to_target[1]), float(to_target[0]))
-        else:
-            target_yaw = 0.0
-
-        robot_yaw = math.radians(float(robot.global_orientation_euler[2]))
-        yaw_error = target_yaw - robot_yaw
-
-        # Main heading objective: face the target direction.
-        # heading_align_reward = 1.0 * math.cos(yaw_error)
-
-        abs_yaw_error = abs(yaw_error)
-        alive_bonus = 2.0 * max(0.0, 1.0 - abs_yaw_error / math.pi)
-        head_toward_bonus = self.reward_head_toward_bonus if abs_yaw_error < math.radians(4.0) else 0.0
-
-        if self.last_yaw_error is None:
-            heading_progress_reward = 0.0
-        else:
-            prev_abs_yaw_error = abs(self.last_yaw_error)
-            yaw_err_delta = prev_abs_yaw_error - abs_yaw_error
-            progress_gate = 1.0 if abs_yaw_error > math.radians(4.0) else 0.0
-            heading_progress_reward =  0.8 * progress_gate * yaw_err_delta
-            heading_progress_reward = float(np.clip(heading_progress_reward, -0.4, 0.4))
-        self.last_yaw_error = yaw_error
-
-        # action_penalty = -0.01 * float(np.linalg.norm(action))
-        smoothness_penalty = -0.05 * float(np.linalg.norm(action - self.last_action_for_reward))
-
-        posture_penalty = -0.6 * tilt_mag
-        # Penalize roll/pitch rotational shake but do not penalize yaw turning directly.
-        ang_vel_penalty = -0.06 * rp_ang_vel_mag
-
-        joint_pos = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        ) * self.train_sim_flip
-        left_hip_roll = float(joint_pos[12])
-        right_hip_roll = float(joint_pos[18])
-
-        left_ankle_roll = float(joint_pos[16])
-        right_ankle_roll = float(joint_pos[22])
-
-        max_leg_roll = 0.15  # 防止劈叉姿势
-        split_penalty = -0.8 * max(0.0, (-left_hip_roll + right_hip_roll - 2 * max_leg_roll) / max_leg_roll)
-        left_hip_yaw = float(joint_pos[13])
-        right_hip_yaw = float(joint_pos[19])
-
-        min_leg_separation = 0.05  # 最小腿间距（防止贴得太近）
-        # 惩罚腿过分靠拢（内收）- 基于两腿间距
-        leg_separation = -left_hip_roll + right_hip_roll
-        inward_penalty = -0.25 * max(0.0, (min_leg_separation - leg_separation) / min_leg_separation)
-        
-        
-        # 脚踝roll角度检测：防止过度外翻或内翻
-        max_ankle_roll = 0.15  # 最大允许的脚踝roll角度
-        
-        # 惩罚脚踝过度外翻/内翻（绝对值过大）
-        ankle_roll_penalty = -0.5 * max(0.0, (abs(left_ankle_roll) + abs(right_ankle_roll) - 2 * max_ankle_roll) / max_ankle_roll)
-        
-        # 惩罚两脚踝roll方向相反（不稳定姿势）
-        ankle_roll_cross_penalty = -0.3 * max(0.0, -(left_ankle_roll * right_ankle_roll))
-
-       # 分别惩罚左右大腿过度转动
-        max_hip_yaw = 0.5  # 最大允许的yaw角度
-        left_hip_yaw_penalty = -0.4 * max(0.0, abs(left_hip_yaw) - max_hip_yaw)
-        right_hip_yaw_penalty = -0.4 * max(0.0, abs(right_hip_yaw) - max_hip_yaw)
-        # 智能交叉腿惩罚：只在站立时惩罚，转身时允许交叉腿
-        yaw_rate = float(np.deg2rad(robot.gyroscope[2]))
-        yaw_rate_abs = abs(yaw_rate)
-
-        # 当转身速度较小时才惩罚交叉腿（站立状态）
-        cross_leg_gate = max(0.0, 1.0 - yaw_rate_abs / math.radians(8.0))
-        hip_yaw_cross_penalty = -1.0 * cross_leg_gate * max(0.0, -(left_hip_yaw * right_hip_yaw)) if left_hip_yaw > 0 and right_hip_yaw < 0 else 0.0
-
-        # Torso-lower-body linkage: reward coordinated turning, punish waist-only spinning.
-        waist_speed = abs(float(joint_speed_rad[10]))
-        lower_body_speed = float(np.mean(np.abs(joint_speed_rad[11:23])))
-        lower_body_follow_ratio = lower_body_speed / (waist_speed + 1e-4)
-        linkage_reward = 0.24 * min(1.0, lower_body_follow_ratio) * min(1.0, waist_speed / 1.2)
-        waist_only_turn_penalty = -0.20 * max(0.0, waist_speed - 1.35 * lower_body_speed)
-
-        # Extra posture linkage in yaw joints to avoid decoupled torso twist.
-        waist_yaw = abs(float(joint_pos_rad[10]))
-        hip_yaw_mean = 0.5 * (abs(float(joint_pos_rad[13])) + abs(float(joint_pos_rad[19])))
-        yaw_link_reward = 0.12 * math.exp(-abs(waist_yaw - hip_yaw_mean) / 0.22)
-
-        target_height = self.initial_height
-        height_error =  height - target_height
-        height_error = height - target_height
-        
-        height_penalty = -(math.exp(12*abs(height_error))-1) if height_error > 0.04 else 0
-
-        # # 在 compute_reward 开头附近，添加高度变化率计算
-        # if not hasattr(self, 'last_height'):
-        #     self.last_height = height
-        #     self.last_height_time = self.step_counter  # 可选，用于时间间隔
-        # height_rate = height - self.last_height  # 正为上升，负为下降
-        # self.last_height = height
-
-        # 惩罚高度下降（负变化率）
-        # height_down_penalty = -5.0 * max(0, -height_rate)  # 系数可调，-height_rate 为正表示下降幅度
-
-        # # 在 compute_reward 中
-        # if self.step_counter > 50:
-        #     avg_prev_action = np.mean(self.prev_action_history, axis=0)
-        #     novelty = float(np.linalg.norm(action - avg_prev_action))
-        #     exploration_bonus = 0.05 * novelty
-        # else:
-        #     exploration_bonus = 0
-
-        # self.prev_action_history[self.history_idx] = action
-        # self.history_idx = (self.history_idx + 1) % 50
-
-
-        total = (
-            # progress_reward  + 
-                alive_bonus + 
-                head_toward_bonus +
-                heading_progress_reward +
-                # lateral_penalty +
-                # action_penalty +
-                smoothness_penalty +
-                posture_penalty
-                + ang_vel_penalty
-                + height_penalty
-                + ankle_roll_penalty
-                + ankle_roll_cross_penalty
-                + split_penalty
-                + inward_penalty
-                # + leg_proximity_penalty
-                + left_hip_yaw_penalty
-                + right_hip_yaw_penalty
-                + hip_yaw_cross_penalty
-                + position_penalty
-                # + linkage_reward
-                # + waist_only_turn_penalty
-                # + yaw_link_reward
-                # + stance_collapse_penalty
-                # + hip_yaw_yaw_cross_penalty
-                # + stance_collapse_penalty
-                #  + cross_leg_penalty
-                #  + exploration_bonus
-                # + height_down_penalty
-                 )
-        # print(height_error, height_penalty)
-        
-        now = time.time()
-        if self.reward_debug_interval_sec > 0 and now - self._reward_debug_last_time >= self.reward_debug_interval_sec:
-            self._reward_debug_last_time = now
-            self._reward_debug_steps_left = max(1, self.reward_debug_burst_steps)
-
-        if self._reward_debug_steps_left > 0:
-            self._reward_debug_steps_left -= 1
-            self.debug_log(
-                f"height_penalty:{height_penalty:.4f},"
-                f"smoothness_penalty:{smoothness_penalty:.4f},"
-                f"posture_penalty:{posture_penalty:.4f},"
-                f"heading_progress_reward:{heading_progress_reward:.4f},"
-                # f"stance_collapse_penalty:{stance_collapse_penalty:.4f},"
-                # f"cross_leg_penalty:{cross_leg_penalty:.4f},"
-                f"ang_vel_penalty:{ang_vel_penalty:.4f},"
-                f"split_penalty:{split_penalty:.4f},"
-                f"ankle_roll_penalty:{ankle_roll_penalty:.4f},"
-                f"ankle_roll_cross_penalty:{ankle_roll_cross_penalty:.4f},"
-                f"left_hip_yaw_penalty:{left_hip_yaw_penalty:.4f},"
-                f"right_hip_yaw_penalty:{right_hip_yaw_penalty:.4f},"
-                f"hip_yaw_cross_penalty:{hip_yaw_cross_penalty:.4f},"
-                f"inward_penalty:{inward_penalty:.4f},"
-                f"position_penalty:{position_penalty:.4f},"
-                # f"linkage_reward:{linkage_reward:.4f},"
-                # f"waist_only_turn_penalty:{waist_only_turn_penalty:.4f},"
-                # f"yaw_link_reward:{yaw_link_reward:.4f}"
-                # f"leg_proximity_penalty:{leg_proximity_penalty:.4f},"
-
-                # f"stance_collapse_penalty:{stance_collapse_penalty:.4f},"
-                # f"hip_yaw_yaw_cross_penalty:{hip_yaw_yaw_cross_penalty:.4f},"
-                #   f"height_down_penalty:{height_down_penalty:.4f}",
-                #   f"exploration_bonus:{exploration_bonus:.4f}"
-                f"alive_bonus:{alive_bonus:.4f},"
-                f"abs_yaw_error:{abs_yaw_error:.4f}"
-                f"total:{total:.4f}"
-                )
-
-        return total
-
-
-
-    def step(self, action):
-
-        r = self.Player.robot
-        max_action_delta =  0.5# Limit how much the action can change from the previous step to encourage smoother motions.
-        if self.previous_action is not None:
-            action = np.clip(action, self.previous_action - max_action_delta, self.previous_action + max_action_delta)
-        action[0:2] = 0
-        action[3] = 4
-        action[7] = -4
-        action[2] = 0
-        action[6] = 0
-        action[4] = 0
-        action[5] = -5
-        action[8] = 0
-        action[9] = 5
-        action[10] = 0
-        # action[12] = -1.0
-        # action[18] = 1.0
-        # action[13] = -1.0
-        # action[19] = 1.0
-        self.previous_action = action.copy()
-
-        self.target_joint_positions = (
-                # self.joint_nominal_position +
-                 self.scaling_factor * action
-        )
-        self.target_joint_positions *= self.train_sim_flip
-
-        for idx, target in enumerate(self.target_joint_positions):
-            r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=80, kd=10
-            )
-
-        self.previous_action = action.copy()
-
-        self.sync()  # run simulation step
-        self.step_counter += 1
-
-        if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
-            self.debug_joint_status()
-
-        current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
-
-        if self.step_counter % 10 == 0:
-            self.previous_pos = current_pos.copy()
-
-        # Compute reward based on movement from previous step
-        reward = self.compute_reward(self.previous_pos, current_pos, action)
-
-        self.last_action_for_reward = action.copy()
-
-        # Fall detection and penalty
-        is_fallen = self.Player.world.global_position[2] < 0.55
-
-        # terminal state: the robot is falling or timeout
-        terminated = is_fallen or self.step_counter > 800 or self.route_completed
-        truncated = False
-
-        return self.observe(), reward, terminated, truncated, {}
-
-
-class Train(Train_Base):
-    def __init__(self, script) -> None:
-        super().__init__(script)
-
-    def train(self, args):
-
-        # --------------------------------------- Learning parameters
-        n_envs = int(os.environ.get("GYM_CPU_N_ENVS", "20"))
-        if n_envs < 1:
-            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
-        server_warmup_sec = float(os.environ.get("GYM_CPU_SERVER_WARMUP_SEC", "3.0"))
-        n_steps_per_env = int(os.environ.get("GYM_CPU_TRAIN_STEPS_PER_ENV", "512"))  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = int(os.environ.get("GYM_CPU_TRAIN_BATCH_SIZE", "512"))  # should be a factor of (n_steps_per_env * n_envs)
-        total_steps = 30000000
-        learning_rate = float(os.environ.get("GYM_CPU_TRAIN_LR", "3e-4"))
-        folder_name = f'Turn_R{self.robot_type}'
-        model_path = f'./scripts/gyms/logs/{folder_name}/'
-
-        print(f"Model path: {model_path}")
-        print(f"Using {n_envs} parallel environments")
-
-        # --------------------------------------- Run algorithm
-        def init_env(i_env, monitor=False):
-            def thunk():
-                env = WalkEnv(self.ip, self.server_p + i_env)
-                if monitor:
-                    env = Monitor(env)
-                return env
-
-            return thunk
-
-        server_log_dir = os.path.join(model_path, "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1, no_render=True, no_realtime=True)  # include 1 extra server for testing
-
-        # Wait for servers to start
-        print(f"Starting {n_envs + 1} rcssservermj servers...")
-        if server_warmup_sec > 0:
-            print(f"Waiting {server_warmup_sec:.1f}s for server warmup...")
-            sleep(server_warmup_sec)
-        print("Servers started, creating environments...")
-
-        env = SubprocVecEnv([init_env(i, monitor=True) for i in range(n_envs)], start_method="spawn")
-        # Use single-process eval env to avoid extra subprocess fragility during callback evaluation.
-        eval_env = DummyVecEnv([init_env(n_envs, monitor=True)])
-
-        try:
-            # Custom policy network architecture
-            policy_kwargs = dict(
-                net_arch=dict(
-                    pi=[512, 256, 128],  # Policy network: 3 layers
-                    vf=[512, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
-            )
-
-            if "model_file" in args:  # retrain
-                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
-                                 batch_size=minibatch_size, learning_rate=learning_rate)
-            else:  # train new model
-                model = PPO(
-                    "MlpPolicy",
-                    env=env,
-                    verbose=1,
-                    n_steps=n_steps_per_env,
-                    batch_size=minibatch_size,
-                    learning_rate=learning_rate,
-                    device="cpu",
-                    policy_kwargs=policy_kwargs,
-                    ent_coef=float(os.environ.get("GYM_CPU_TRAIN_ENT_COEF", "0.05")),  # Entropy coefficient for exploration
-                    clip_range=float(os.environ.get("GYM_CPU_TRAIN_CLIP_RANGE", "0.2")),  # PPO clipping parameter
-                    gae_lambda=0.95,  # GAE lambda
-                    gamma=float(os.environ.get("GYM_CPU_TRAIN_GAMMA", "0.95")), # Discount factor
-                    # target_kl=0.03,
-                    n_epochs=int(os.environ.get("GYM_CPU_TRAIN_EPOCHS", "5")),
-                    tensorboard_log=f"./scripts/gyms/logs/{folder_name}/tensorboard/"
-                )
-
-            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
-                                          eval_freq=n_steps_per_env * 20, save_freq=n_steps_per_env * 20, eval_eps=7,
-                                          backup_env_file=__file__)
-        except KeyboardInterrupt:
-            sleep(1)  # wait for child processes
-            print("\nctrl+c pressed, aborting...\n")
-            servers.kill()
-            return
-
-        env.close()
-        eval_env.close()
-        servers.kill()
-
-    def test(self, args):
-
-        # Uses different server and monitor ports
-        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        test_no_render = os.environ.get("GYM_CPU_TEST_NO_RENDER", "0") == "1"
-        test_no_realtime = os.environ.get("GYM_CPU_TEST_NO_REALTIME", "0") == "1"
-
-        server = Train_Server(
-            self.server_p - 1,
-            self.monitor_p,
-            1,
-            no_render=test_no_render,
-            no_realtime=test_no_realtime,
-        )
-        env = WalkEnv(self.ip, self.server_p - 1)
-        model = PPO.load(args["model_file"], env=env)
-
-        try:
-            self.export_model(args["model_file"], args["model_file"] + ".pkl",
-                              False)  # Export to pkl to create custom behavior
-            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
-        except KeyboardInterrupt:
-            print()
-
-        env.close()
-        server.kill()
-
-
-if __name__ == "__main__":
-    from types import SimpleNamespace
-
-    # 创建默认参数
-    script_args = SimpleNamespace(
-        args=SimpleNamespace(
-            i='127.0.0.1',  # Server IP
-            p=3100,  # Server port
-            m=3200,  # Monitor port
-            r=0,  # Robot type
-            t='Gym',  # Team name
-            u=1  # Uniform number
-        )
-    )
-
-    trainer = Train(script_args)
-
-    run_mode = os.environ.get("GYM_CPU_MODE", "train").strip().lower()
-
-    if run_mode == "test":
-        test_model_file = os.environ.get("GYM_CPU_TEST_MODEL", "scripts/gyms/logs/Turn_R0_004/best_model.zip")
-        test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Turn_R0_004/")
-        trainer.test({"model_file": test_model_file, "folder_dir": test_folder})
-    else:
-        retrain_model = os.environ.get("GYM_CPU_TRAIN_MODEL", "").strip()
-        if retrain_model:
-            trainer.train({"model_file": retrain_model})
-        else:
-            trainer.train({})

scripts/gyms/logs/Turn_R0_009/Walk.py

@@ -1,853 +0,0 @@
-import os
-import numpy as np
-import math
-import time
-from time import sleep
-from random import random
-from random import uniform
-from itertools import count
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.monitor import Monitor
-from stable_baselines3.common.vec_env import SubprocVecEnv, DummyVecEnv
-
-import gymnasium as gym
-from gymnasium import spaces
-
-from scripts.commons.Train_Base import Train_Base
-from scripts.commons.Server import Server as Train_Server
-
-from agent.base_agent import Base_Agent
-from utils.math_ops import MathOps
-
-from scipy.spatial.transform import Rotation as R
-
-'''
-Objective:
-Learn how to run forward using step primitive
-----------
-- class Basic_Run: implements an OpenAI custom gym
-- class Train:  implements algorithms to train a new model or test an existing model
-'''
-
-
-class WalkEnv(gym.Env):
-    def __init__(self, ip, server_p) -> None:
-
-        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
-        self.Player = player = Base_Agent(
-            team_name="Gym",
-            number=1,
-            host=ip,
-            port=server_p
-        )
-        self.robot_type = self.Player.robot
-        self.step_counter = 0  # to limit episode size
-        self.force_play_on = True
-
-        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
-        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
-        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
-        self.isfallen = False
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.debug_every_n_steps = 5
-        self.enable_debug_joint_status = False
-        self.reward_debug_interval_sec = float(os.environ.get("GYM_CPU_REWARD_DEBUG_INTERVAL_SEC", "600"))
-        self.reward_debug_burst_steps = int(os.environ.get("GYM_CPU_REWARD_DEBUG_BURST_STEPS", "10"))
-        self._reward_debug_last_time = time.time()
-        self._reward_debug_steps_left = 0
-        self.calibrate_nominal_from_neutral = True
-        self.auto_calibrate_train_sim_flip = True
-        self.nominal_calibrated_once = False
-        self.flip_calibrated_once = False
-        self._target_hz = 0.0
-        self._target_dt = 0.0
-        self._last_sync_time = None
-        target_hz_env = 0
-        if target_hz_env:
-            try:
-                self._target_hz = float(target_hz_env)
-            except ValueError:
-                self._target_hz = 0.0
-        if self._target_hz > 0.0:
-            self._target_dt = 1.0 / self._target_hz
-
-        # State space
-        # 原始观测大小: 78
-        obs_size = 78
-        self.obs = np.zeros(obs_size, np.float32)
-        self.observation_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(obs_size,),
-            dtype=np.float32
-        )
-
-        action_dim = len(self.Player.robot.ROBOT_MOTORS)
-        self.no_of_actions = action_dim
-        self.action_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(action_dim,),
-            dtype=np.float32
-        )
-
-        # 中立姿态
-        self.joint_nominal_position = np.array(
-            [
-                0.0,  # 0: Head_yaw (he1)
-                0.0,  # 1: Head_pitch (he2)
-                0.0,  # 2: Left_Shoulder_Pitch (lae1)
-                0.0,  # 3: Left_Shoulder_Roll (lae2)
-                0.0,  # 4: Left_Elbow_Pitch (lae3)
-                0.0,  # 5: Left_Elbow_Yaw (lae4)
-                0.0,  # 6: Right_Shoulder_Pitch (rae1)
-                0.0,  # 7: Right_Shoulder_Roll (rae2)
-                0.0,  # 8: Right_Elbow_Pitch (rae3)
-                0.0,  # 9: Right_Elbow_Yaw (rae4)
-                0.0,  # 10: Waist (te1)
-                0.0,  # 11: Left_Hip_Pitch (lle1)
-                0.0,  # 12: Left_Hip_Roll (lle2)
-                1.0,  # 13: Left_Hip_Yaw (lle3)
-                0.0,  # 14: Left_Knee_Pitch (lle4)
-                0.0,  # 15: Left_Ankle_Pitch (lle5)
-                0.0,  # 16: Left_Ankle_Roll (lle6)
-                0.0,  # 17: Right_Hip_Pitch (rle1)
-                0.0,  # 18: Right_Hip_Roll (rle2)
-                1.0,  # 19: Right_Hip_Yaw (rle3)
-                0.0,  # 20: Right_Knee_Pitch (rle4)
-                0.0,  # 21: Right_Ankle_Pitch (rle5)
-                0.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-        self.joint_nominal_position = np.zeros(self.no_of_actions)
-        self.train_sim_flip = np.array(
-            [
-                1.0,  # 0: Head_yaw (he1)
-                -1.0,  # 1: Head_pitch (he2)
-                1.0,  # 2: Left_Shoulder_Pitch (lae1)
-                -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                -1.0,  # 4: Left_Elbow_Pitch (lae3)
-                1.0,  # 5: Left_Elbow_Yaw (lae4)
-                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                -1.0,  # 7: Right_Shoulder_Roll (rae2)
-                1.0,  # 8: Right_Elbow_Pitch (rae3)
-                1.0,  # 9: Right_Elbow_Yaw (rae4)
-                1.0,  # 10: Waist (te1)
-                1.0,  # 11: Left_Hip_Pitch (lle1)
-                -1.0,  # 12: Left_Hip_Roll (lle2)
-                -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,  # 14: Left_Knee_Pitch (lle4)
-                1.0,  # 15: Left_Ankle_Pitch (lle5)
-                -1.0,  # 16: Left_Ankle_Roll (lle6)
-                -1.0,  # 17: Right_Hip_Pitch (rle1)
-                -1.0,  # 18: Right_Hip_Roll (rle2)
-                -1.0,  # 19: Right_Hip_Yaw (rle3)
-                -1.0,  # 20: Right_Knee_Pitch (rle4)
-                -1.0,  # 21: Right_Ankle_Pitch (rle5)
-                -1.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-
-        self.scaling_factor = 0.3
-        # self.scaling_factor = 1
-
-        # Encourage a minimum lateral stance so the policy avoids feet overlap.
-        self.min_stance_rad = 0.10
-
-        # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = False
-        self.reset_beam_yaw_range_deg = float(os.environ.get("GYM_CPU_RESET_BEAM_YAW_RANGE_DEG", "180"))
-        self.reset_target_bearing_range_deg = float(os.environ.get("GYM_CPU_RESET_TARGET_BEARING_RANGE_DEG", "45"))
-        self.reset_target_distance_min = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MIN", "1.2"))
-        self.reset_target_distance_max = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MAX", "2.8"))
-        if self.reset_target_distance_min > self.reset_target_distance_max:
-            self.reset_target_distance_min, self.reset_target_distance_max = (
-                self.reset_target_distance_max,
-                self.reset_target_distance_min,
-            )
-        self.reset_joint_noise_rad = 0.025
-        self.reset_perturb_steps = 4
-        self.reset_recover_steps = 8
-
-        self.reward_smoothness_scale = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_SCALE", "0.06"))
-        self.reward_smoothness_cap = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_CAP", "0.45"))
-        self.reward_head_toward_bonus = float(os.environ.get("GYM_CPU_REWARD_HEAD_TOWARD_BONUS", "1"))
-
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Track previous position
-        self.last_yaw_error = None
-        self.Player.server.connect()
-        # sleep(2.0)  # Longer wait for connection to establish completely
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-        self.start_time = time.time()
-
-    def _reconnect_server(self):
-        try:
-            self.Player.server.shutdown()
-        except Exception:
-            pass
-
-        self.Player.server.connect()
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-
-    def _safe_receive_world_update(self, retries=1):
-        last_exc = None
-        for attempt in range(retries + 1):
-            try:
-                self.Player.server.receive()
-                self.Player.world.update()
-                return
-            except (ConnectionResetError, OSError) as exc:
-                last_exc = exc
-                if attempt >= retries:
-                    raise
-                self._reconnect_server()
-        if last_exc is not None:
-            raise last_exc
-
-    def debug_log(self, message):
-        print(message)
-        try:
-            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
-            with open(log_path, "a", encoding="utf-8") as f:
-                f.write(message + "\n")
-        except OSError:
-            pass
-
-    @staticmethod
-    def _wrap_to_pi(angle_rad: float) -> float:
-        return (angle_rad + math.pi) % (2.0 * math.pi) - math.pi
-
-    def observe(self, init=False):
-
-        """获取当前观测值"""
-        robot = self.Player.robot
-        world = self.Player.world
-
-        # Safety check: ensure data is available
-
-        # 计算目标速度
-        raw_target = self.target_position - world.global_position[:2]
-        velocity = MathOps.rotate_2d_vec(
-            raw_target,
-            -robot.global_orientation_euler[2],
-            is_rad=False
-        )
-
-        # 计算相对方向
-        rel_orientation = MathOps.vector_angle(velocity) * 0.3
-        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
-
-        velocity = np.concatenate([velocity, np.array([rel_orientation])])
-        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
-        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
-
-        # 关节状态
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        qpos_qvel_previous_action = np.concatenate([
-            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
-            radian_joint_speeds / 110.0 * self.train_sim_flip,
-            self.previous_action / 10.0,
-        ])
-
-        # 角速度
-        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
-
-        # 投影的重力方向
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-
-        # 组合观测
-        observation = np.concatenate([
-            qpos_qvel_previous_action,
-            ang_vel,
-            velocity,
-            projected_gravity,
-        ])
-
-        observation = np.clip(observation, -10.0, 10.0)
-        return observation.astype(np.float32)
-
-    def sync(self):
-        ''' Run a single simulation step '''
-        self._safe_receive_world_update(retries=1)
-        self.Player.robot.commit_motor_targets_pd()
-        self.Player.server.send()
-        if self._target_dt > 0.0:
-            now = time.time()
-            if self._last_sync_time is None:
-                self._last_sync_time = now
-                return
-            elapsed = now - self._last_sync_time
-            remaining = self._target_dt - elapsed
-            if remaining > 0.0:
-                time.sleep(remaining)
-                now = time.time()
-            self._last_sync_time = now
-
-    def debug_joint_status(self):
-        robot = self.Player.robot
-        actual_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        target_joint_positions = getattr(
-            self,
-            'target_joint_positions',
-            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
-        )
-        joint_error = actual_joint_positions - target_joint_positions
-        leg_slice = slice(11, None)
-
-        self.debug_log(
-            "[WalkDebug] "
-            f"step={self.step_counter} "
-            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
-            f"target_xy={np.round(self.target_position, 3).tolist()} "
-            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
-            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
-            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
-            f"fallen={self.Player.world.global_position[2] < 0.3}"
-        )
-        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
-
-    def reset(self, seed=None, options=None):
-        '''
-        Reset and stabilize the robot
-        Note: for some behaviors it would be better to reduce stabilization or add noise
-        '''
-        r = self.Player.robot
-        super().reset(seed=seed)
-        if seed is not None:
-            np.random.seed(seed)
-
-        target_distance = np.random.uniform(self.reset_target_distance_min, self.reset_target_distance_max)
-        target_bearing_deg = np.random.uniform(-self.reset_target_bearing_range_deg, self.reset_target_bearing_range_deg)
-
-        self.step_counter = 0
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
-        self.last_yaw_error = None
-        self.walk_cycle_step = 0
-        self._reward_debug_steps_left = 0
-
-        # 随机 beam 目标位置和朝向，增加训练多样性
-        beam_x = (random() - 0.5) * 10
-        beam_y = (random() - 0.5) * 10
-        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
-
-        for _ in range(5):
-            self._safe_receive_world_update(retries=2)
-            self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
-            self.Player.server.send()
-
-        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
-        finished_count = 0
-        for _ in range(50):
-            finished = self.Player.skills_manager.execute("Neutral")
-            self.sync()
-            if finished:
-                finished_count += 1
-                if finished_count >= 20:  # 假设需要连续20次完成才算成功
-                    break
-
-        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-            # Perturb waist + lower body only (10:), keep head/arms stable.
-            perturb_action[10:] = np.random.uniform(
-                -self.reset_joint_noise_rad,
-                self.reset_joint_noise_rad,
-                size=(self.no_of_actions - 10,)
-            )
-
-            for _ in range(self.reset_perturb_steps):
-                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-            for i in range(self.reset_recover_steps):
-                # Linearly fade perturbation to help policy start from near-neutral.
-                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-        # memory variables
-        self.sync()  
-        self.initial_position = np.array(self.Player.world.global_position[:2])
-        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions, np.float32)
-        # Randomize global target bearing so policy must learn to rotate toward it first.
-        heading_deg = float(r.global_orientation_euler[2])
-        target_offset = MathOps.rotate_2d_vec(
-            np.array([target_distance, 0.0]),
-            heading_deg + target_bearing_deg,
-            is_rad=False,
-        )
-        point1 = self.initial_position + target_offset
-        self.point_list = [point1]
-        self.target_position = self.point_list[self.waypoint_index]
-        self.initial_height = self.Player.world.global_position[2]
-
-        return self.observe(True), {}
-
-    def render(self, mode='human', close=False):
-        return
-
-    
-    def compute_reward(self, previous_pos, current_pos, action):
-        height = float(self.Player.world.global_position[2])
-        robot = self.Player.robot
-
-
-        joint_pos_rad = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        joint_speed_rad = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-        tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        ang_vel = np.deg2rad(robot.gyroscope)
-        rp_ang_vel_mag = float(np.linalg.norm(ang_vel[:2]))
-
-        is_fallen = height < 0.55
-        if is_fallen:
-            # remain = max(0, 800 - self.step_counter)
-            # return -8.0 - 0.01 * remain
-            return -20.0
-        
-
-        if np.linalg.norm(current_pos - previous_pos) > 0.005:
-            position_penalty = -3 * float(np.linalg.norm(current_pos - previous_pos))
-        else:
-            position_penalty = 0.0
-
-
-        # Turn-to-target shaping.
-        to_target = self.target_position - current_pos
-        dist_to_target = float(np.linalg.norm(to_target))
-        if dist_to_target > 1e-6:
-            target_yaw = math.atan2(float(to_target[1]), float(to_target[0]))
-        else:
-            target_yaw = 0.0
-
-        robot_yaw = math.radians(float(robot.global_orientation_euler[2]))
-        yaw_error = target_yaw - robot_yaw
-
-        # Main heading objective: face the target direction.
-        # heading_align_reward = 1.0 * math.cos(yaw_error)
-
-        abs_yaw_error = abs(yaw_error)
-        alive_bonus = 2.0 * max(0.0, 1.0 - abs_yaw_error / math.pi)
-        head_toward_bonus = self.reward_head_toward_bonus if abs_yaw_error < math.radians(4.0) else 0.0
-
-        if self.last_yaw_error is None:
-            heading_progress_reward = 0.0
-        else:
-            prev_abs_yaw_error = abs(self.last_yaw_error)
-            yaw_err_delta = prev_abs_yaw_error - abs_yaw_error
-            progress_gate = 1.0 if abs_yaw_error > math.radians(4.0) else 0.0
-            heading_progress_reward =  0.8 * progress_gate * yaw_err_delta
-            heading_progress_reward = float(np.clip(heading_progress_reward, -0.4, 0.4))
-        self.last_yaw_error = yaw_error
-
-        # action_penalty = -0.01 * float(np.linalg.norm(action))
-        smoothness_penalty = -0.05 * float(np.linalg.norm(action - self.last_action_for_reward))
-
-        posture_penalty = -0.6 * tilt_mag
-        # Penalize roll/pitch rotational shake but do not penalize yaw turning directly.
-        ang_vel_penalty = -0.06 * rp_ang_vel_mag
-
-        joint_pos = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        ) * self.train_sim_flip
-        left_hip_roll = float(joint_pos[12])
-        right_hip_roll = float(joint_pos[18])
-        left_hip_pitch = float(joint_pos[11])
-        right_hip_pitch = float(joint_pos[17])
-
-        left_ankle_roll = float(joint_pos[16])
-        right_ankle_roll = float(joint_pos[22])
-
-        max_leg_roll = 0.15  # 防止劈叉姿势
-        split_penalty = -0.8 * max(0.0, (-left_hip_roll + right_hip_roll - 2 * max_leg_roll) / max_leg_roll)
-        left_hip_yaw = float(joint_pos[13])
-        right_hip_yaw = float(joint_pos[19])
-
-        min_leg_separation = 0.05  # 最小腿间距（防止贴得太近）
-        # 惩罚腿过分靠拢（内收）- 基于两腿间距
-        leg_separation = -left_hip_roll + right_hip_roll
-        inward_penalty = -0.25 * max(0.0, (min_leg_separation - leg_separation) / min_leg_separation)
-        
-        
-        # 脚踝roll角度检测：防止过度外翻或内翻
-        max_ankle_roll = 0.15  # 最大允许的脚踝roll角度
-        
-        # 惩罚脚踝过度外翻/内翻（绝对值过大）
-        ankle_roll_penalty = -0.5 * max(0.0, (abs(left_ankle_roll) + abs(right_ankle_roll) - 2 * max_ankle_roll) / max_ankle_roll)
-        
-        # 惩罚两脚踝roll方向相反（不稳定姿势）
-        ankle_roll_cross_penalty = -0.3 * max(0.0, -(left_ankle_roll * right_ankle_roll))
-
-       # 分别惩罚左右大腿过度转动
-        max_hip_yaw = 0.3  # 最大允许的yaw角度
-        left_hip_yaw_penalty = -0.4 * max(0.0, abs(left_hip_yaw) - max_hip_yaw)
-        right_hip_yaw_penalty = -0.4 * max(0.0, abs(right_hip_yaw) - max_hip_yaw)
-        # 智能交叉腿惩罚：只在站立时惩罚，转身时允许交叉腿
-        yaw_rate = float(np.deg2rad(robot.gyroscope[2]))
-        yaw_rate_abs = abs(yaw_rate)
-
-        # 当转身速度较小时才惩罚交叉腿（站立状态）
-        cross_leg_gate = max(0.0, 1.0 - yaw_rate_abs / math.radians(8.0))
-        hip_yaw_cross_penalty = -1.0 * cross_leg_gate * max(0.0, -(left_hip_yaw * right_hip_yaw)) if left_hip_yaw > 0 and right_hip_yaw < 0 else 0.0
-
-        # Torso-lower-body linkage: reward coordinated turning, punish waist-only spinning.
-        waist_speed = abs(float(joint_speed_rad[10]))
-        lower_body_speed = float(np.mean(np.abs(joint_speed_rad[11:23])))
-        lower_body_follow_ratio = lower_body_speed / (waist_speed + 1e-4)
-        linkage_reward = 0.24 * min(1.0, lower_body_follow_ratio) * min(1.0, waist_speed / 1.2)
-        waist_only_turn_penalty = -0.20 * max(0.0, waist_speed - 1.35 * lower_body_speed)
-
-        # Extra posture linkage in yaw joints to avoid decoupled torso twist.
-        waist_yaw = abs(float(joint_pos_rad[10]))
-        hip_yaw_mean = 0.5 * (abs(float(joint_pos_rad[13])) + abs(float(joint_pos_rad[19])))
-        yaw_link_reward = 0.12 * math.exp(-abs(waist_yaw - hip_yaw_mean) / 0.22)
-
-        target_height = self.initial_height
-        height_error =  height - target_height
-        height_error = height - target_height
-        
-        height_penalty = -(math.exp(12*abs(height_error))-1) if height_error > 0.04 else 0
-
-        # # 在 compute_reward 开头附近，添加高度变化率计算
-        # if not hasattr(self, 'last_height'):
-        #     self.last_height = height
-        #     self.last_height_time = self.step_counter  # 可选，用于时间间隔
-        # height_rate = height - self.last_height  # 正为上升，负为下降
-        # self.last_height = height
-
-        # 惩罚高度下降（负变化率）
-        # height_down_penalty = -5.0 * max(0, -height_rate)  # 系数可调，-height_rate 为正表示下降幅度
-
-        # # 在 compute_reward 中
-        # if self.step_counter > 50:
-        #     avg_prev_action = np.mean(self.prev_action_history, axis=0)
-        #     novelty = float(np.linalg.norm(action - avg_prev_action))
-        #     exploration_bonus = 0.05 * novelty
-        # else:
-        #     exploration_bonus = 0
-
-        # self.prev_action_history[self.history_idx] = action
-        # self.history_idx = (self.history_idx + 1) % 50
-
-
-        total = (
-            # progress_reward  + 
-                alive_bonus + 
-                head_toward_bonus +
-                heading_progress_reward +
-                # lateral_penalty +
-                # action_penalty +
-                smoothness_penalty +
-                posture_penalty
-                + ang_vel_penalty
-                + height_penalty
-                + ankle_roll_penalty
-                + ankle_roll_cross_penalty
-                + split_penalty
-                + inward_penalty
-                # + leg_proximity_penalty
-                + left_hip_yaw_penalty
-                + right_hip_yaw_penalty
-                + hip_yaw_cross_penalty
-                + position_penalty
-                # + linkage_reward
-                # + waist_only_turn_penalty
-                # + yaw_link_reward
-                # + stance_collapse_penalty
-                # + hip_yaw_yaw_cross_penalty
-                # + stance_collapse_penalty
-                #  + cross_leg_penalty
-                #  + exploration_bonus
-                # + height_down_penalty
-                 )
-        # print(height_error, height_penalty)
-        
-        now = time.time()
-        if self.reward_debug_interval_sec > 0 and now - self._reward_debug_last_time >= self.reward_debug_interval_sec:
-            self._reward_debug_last_time = now
-            self._reward_debug_steps_left = max(1, self.reward_debug_burst_steps)
-
-        if self._reward_debug_steps_left > 0:
-            self._reward_debug_steps_left -= 1
-            self.debug_log(
-                f"height_penalty:{height_penalty:.4f},"
-                f"smoothness_penalty:{smoothness_penalty:.4f},"
-                f"posture_penalty:{posture_penalty:.4f},"
-                f"heading_progress_reward:{heading_progress_reward:.4f},"
-                # f"stance_collapse_penalty:{stance_collapse_penalty:.4f},"
-                # f"cross_leg_penalty:{cross_leg_penalty:.4f},"
-                f"ang_vel_penalty:{ang_vel_penalty:.4f},"
-                f"split_penalty:{split_penalty:.4f},"
-                f"ankle_roll_penalty:{ankle_roll_penalty:.4f},"
-                f"ankle_roll_cross_penalty:{ankle_roll_cross_penalty:.4f},"
-                f"left_hip_yaw_penalty:{left_hip_yaw_penalty:.4f},"
-                f"right_hip_yaw_penalty:{right_hip_yaw_penalty:.4f},"
-                f"hip_yaw_cross_penalty:{hip_yaw_cross_penalty:.4f},"
-                f"inward_penalty:{inward_penalty:.4f},"
-                f"position_penalty:{position_penalty:.4f},"
-                # f"linkage_reward:{linkage_reward:.4f},"
-                # f"waist_only_turn_penalty:{waist_only_turn_penalty:.4f},"
-                # f"yaw_link_reward:{yaw_link_reward:.4f}"
-                # f"leg_proximity_penalty:{leg_proximity_penalty:.4f},"
-
-                # f"stance_collapse_penalty:{stance_collapse_penalty:.4f},"
-                # f"hip_yaw_yaw_cross_penalty:{hip_yaw_yaw_cross_penalty:.4f},"
-                #   f"height_down_penalty:{height_down_penalty:.4f}",
-                #   f"exploration_bonus:{exploration_bonus:.4f}"
-                f"alive_bonus:{alive_bonus:.4f},"
-                f"abs_yaw_error:{abs_yaw_error:.4f}"
-                f"total:{total:.4f}"
-                )
-
-        return total
-
-
-
-    def step(self, action):
-
-        r = self.Player.robot
-        max_action_delta =  0.5# Limit how much the action can change from the previous step to encourage smoother motions.
-        if self.previous_action is not None:
-            action = np.clip(action, self.previous_action - max_action_delta, self.previous_action + max_action_delta)
-        action[0:2] = 0
-        action[3] = 4
-        action[7] = -4
-        action[2] = 0
-        action[6] = 0
-        action[4] = 0
-        action[5] = -5
-        action[8] = 0
-        action[9] = 5
-        action[10] = 0
-        action[11] = np.clip(action[11], -0.3, 0.3)
-        action[17] = np.clip(action[17], -0.3, 0.3)
-        # action[12] = -1.0
-        # action[18] = 1.0
-        # action[13] = -1.0
-        # action[19] = 1.0
-        self.previous_action = action.copy()
-
-        self.target_joint_positions = (
-                # self.joint_nominal_position +
-                 self.scaling_factor * action
-        )
-        self.target_joint_positions *= self.train_sim_flip
-
-        for idx, target in enumerate(self.target_joint_positions):
-            r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=80, kd=10
-            )
-
-        self.previous_action = action.copy()
-
-        self.sync()  # run simulation step
-        self.step_counter += 1
-
-        if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
-            self.debug_joint_status()
-
-        current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
-
-        if self.step_counter % 10 == 0:
-            self.previous_pos = current_pos.copy()
-
-        # Compute reward based on movement from previous step
-        reward = self.compute_reward(self.previous_pos, current_pos, action)
-
-        self.last_action_for_reward = action.copy()
-
-        # Fall detection and penalty
-        is_fallen = self.Player.world.global_position[2] < 0.55
-
-        # terminal state: the robot is falling or timeout
-        terminated = is_fallen or self.step_counter > 800 or self.route_completed
-        truncated = False
-
-        return self.observe(), reward, terminated, truncated, {}
-
-
-class Train(Train_Base):
-    def __init__(self, script) -> None:
-        super().__init__(script)
-
-    def train(self, args):
-
-        # --------------------------------------- Learning parameters
-        n_envs = int(os.environ.get("GYM_CPU_N_ENVS", "20"))
-        if n_envs < 1:
-            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
-        server_warmup_sec = float(os.environ.get("GYM_CPU_SERVER_WARMUP_SEC", "3.0"))
-        n_steps_per_env = int(os.environ.get("GYM_CPU_TRAIN_STEPS_PER_ENV", "512"))  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = int(os.environ.get("GYM_CPU_TRAIN_BATCH_SIZE", "512"))  # should be a factor of (n_steps_per_env * n_envs)
-        total_steps = 30000000
-        learning_rate = float(os.environ.get("GYM_CPU_TRAIN_LR", "3e-4"))
-        folder_name = f'Turn_R{self.robot_type}'
-        model_path = f'./scripts/gyms/logs/{folder_name}/'
-
-        print(f"Model path: {model_path}")
-        print(f"Using {n_envs} parallel environments")
-
-        # --------------------------------------- Run algorithm
-        def init_env(i_env, monitor=False):
-            def thunk():
-                env = WalkEnv(self.ip, self.server_p + i_env)
-                if monitor:
-                    env = Monitor(env)
-                return env
-
-            return thunk
-
-        server_log_dir = os.path.join(model_path, "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1, no_render=True, no_realtime=True)  # include 1 extra server for testing
-
-        # Wait for servers to start
-        print(f"Starting {n_envs + 1} rcssservermj servers...")
-        if server_warmup_sec > 0:
-            print(f"Waiting {server_warmup_sec:.1f}s for server warmup...")
-            sleep(server_warmup_sec)
-        print("Servers started, creating environments...")
-
-        env = SubprocVecEnv([init_env(i, monitor=True) for i in range(n_envs)], start_method="spawn")
-        # Use single-process eval env to avoid extra subprocess fragility during callback evaluation.
-        eval_env = DummyVecEnv([init_env(n_envs, monitor=True)])
-
-        try:
-            # Custom policy network architecture
-            policy_kwargs = dict(
-                net_arch=dict(
-                    pi=[512, 256, 128],  # Policy network: 3 layers
-                    vf=[512, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
-            )
-
-            if "model_file" in args:  # retrain
-                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
-                                 batch_size=minibatch_size, learning_rate=learning_rate)
-            else:  # train new model
-                model = PPO(
-                    "MlpPolicy",
-                    env=env,
-                    verbose=1,
-                    n_steps=n_steps_per_env,
-                    batch_size=minibatch_size,
-                    learning_rate=learning_rate,
-                    device="cpu",
-                    policy_kwargs=policy_kwargs,
-                    ent_coef=float(os.environ.get("GYM_CPU_TRAIN_ENT_COEF", "0.05")),  # Entropy coefficient for exploration
-                    clip_range=float(os.environ.get("GYM_CPU_TRAIN_CLIP_RANGE", "0.2")),  # PPO clipping parameter
-                    gae_lambda=0.95,  # GAE lambda
-                    gamma=float(os.environ.get("GYM_CPU_TRAIN_GAMMA", "0.95")), # Discount factor
-                    # target_kl=0.03,
-                    n_epochs=int(os.environ.get("GYM_CPU_TRAIN_EPOCHS", "5")),
-                    tensorboard_log=f"./scripts/gyms/logs/{folder_name}/tensorboard/"
-                )
-
-            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
-                                          eval_freq=n_steps_per_env * 20, save_freq=n_steps_per_env * 20, eval_eps=7,
-                                          backup_env_file=__file__)
-        except KeyboardInterrupt:
-            sleep(1)  # wait for child processes
-            print("\nctrl+c pressed, aborting...\n")
-            servers.kill()
-            return
-
-        env.close()
-        eval_env.close()
-        servers.kill()
-
-    def test(self, args):
-
-        # Uses different server and monitor ports
-        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        test_no_render = os.environ.get("GYM_CPU_TEST_NO_RENDER", "0") == "1"
-        test_no_realtime = os.environ.get("GYM_CPU_TEST_NO_REALTIME", "0") == "1"
-
-        server = Train_Server(
-            self.server_p - 1,
-            self.monitor_p,
-            1,
-            no_render=test_no_render,
-            no_realtime=test_no_realtime,
-        )
-        env = WalkEnv(self.ip, self.server_p - 1)
-        model = PPO.load(args["model_file"], env=env)
-
-        try:
-            self.export_model(args["model_file"], args["model_file"] + ".pkl",
-                              False)  # Export to pkl to create custom behavior
-            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
-        except KeyboardInterrupt:
-            print()
-
-        env.close()
-        server.kill()
-
-
-if __name__ == "__main__":
-    from types import SimpleNamespace
-
-    # 创建默认参数
-    script_args = SimpleNamespace(
-        args=SimpleNamespace(
-            i='127.0.0.1',  # Server IP
-            p=3100,  # Server port
-            m=3200,  # Monitor port
-            r=0,  # Robot type
-            t='Gym',  # Team name
-            u=1  # Uniform number
-        )
-    )
-
-    trainer = Train(script_args)
-
-    run_mode = os.environ.get("GYM_CPU_MODE", "train").strip().lower()
-
-    if run_mode == "test":
-        test_model_file = os.environ.get("GYM_CPU_TEST_MODEL", "scripts/gyms/logs/Turn_R0_004/best_model.zip")
-        test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Turn_R0_004/")
-        trainer.test({"model_file": test_model_file, "folder_dir": test_folder})
-    else:
-        retrain_model = os.environ.get("GYM_CPU_TRAIN_MODEL", "").strip()
-        if retrain_model:
-            trainer.train({"model_file": retrain_model})
-        else:
-            trainer.train({})

scripts/gyms/logs/Turn_R0_010/Walk.py

@@ -1,853 +0,0 @@
-import os
-import numpy as np
-import math
-import time
-from time import sleep
-from random import random
-from random import uniform
-from itertools import count
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.monitor import Monitor
-from stable_baselines3.common.vec_env import SubprocVecEnv, DummyVecEnv
-
-import gymnasium as gym
-from gymnasium import spaces
-
-from scripts.commons.Train_Base import Train_Base
-from scripts.commons.Server import Server as Train_Server
-
-from agent.base_agent import Base_Agent
-from utils.math_ops import MathOps
-
-from scipy.spatial.transform import Rotation as R
-
-'''
-Objective:
-Learn how to run forward using step primitive
-----------
-- class Basic_Run: implements an OpenAI custom gym
-- class Train:  implements algorithms to train a new model or test an existing model
-'''
-
-
-class WalkEnv(gym.Env):
-    def __init__(self, ip, server_p) -> None:
-
-        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
-        self.Player = player = Base_Agent(
-            team_name="Gym",
-            number=1,
-            host=ip,
-            port=server_p
-        )
-        self.robot_type = self.Player.robot
-        self.step_counter = 0  # to limit episode size
-        self.force_play_on = True
-
-        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
-        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
-        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
-        self.isfallen = False
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.debug_every_n_steps = 5
-        self.enable_debug_joint_status = False
-        self.reward_debug_interval_sec = float(os.environ.get("GYM_CPU_REWARD_DEBUG_INTERVAL_SEC", "600"))
-        self.reward_debug_burst_steps = int(os.environ.get("GYM_CPU_REWARD_DEBUG_BURST_STEPS", "10"))
-        self._reward_debug_last_time = time.time()
-        self._reward_debug_steps_left = 0
-        self.calibrate_nominal_from_neutral = True
-        self.auto_calibrate_train_sim_flip = True
-        self.nominal_calibrated_once = False
-        self.flip_calibrated_once = False
-        self._target_hz = 0.0
-        self._target_dt = 0.0
-        self._last_sync_time = None
-        target_hz_env = 0
-        if target_hz_env:
-            try:
-                self._target_hz = float(target_hz_env)
-            except ValueError:
-                self._target_hz = 0.0
-        if self._target_hz > 0.0:
-            self._target_dt = 1.0 / self._target_hz
-
-        # State space
-        # 原始观测大小: 78
-        obs_size = 78
-        self.obs = np.zeros(obs_size, np.float32)
-        self.observation_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(obs_size,),
-            dtype=np.float32
-        )
-
-        action_dim = len(self.Player.robot.ROBOT_MOTORS)
-        self.no_of_actions = action_dim
-        self.action_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(action_dim,),
-            dtype=np.float32
-        )
-
-        # 中立姿态
-        self.joint_nominal_position = np.array(
-            [
-                0.0,  # 0: Head_yaw (he1)
-                0.0,  # 1: Head_pitch (he2)
-                0.0,  # 2: Left_Shoulder_Pitch (lae1)
-                0.0,  # 3: Left_Shoulder_Roll (lae2)
-                0.0,  # 4: Left_Elbow_Pitch (lae3)
-                0.0,  # 5: Left_Elbow_Yaw (lae4)
-                0.0,  # 6: Right_Shoulder_Pitch (rae1)
-                0.0,  # 7: Right_Shoulder_Roll (rae2)
-                0.0,  # 8: Right_Elbow_Pitch (rae3)
-                0.0,  # 9: Right_Elbow_Yaw (rae4)
-                0.0,  # 10: Waist (te1)
-                0.0,  # 11: Left_Hip_Pitch (lle1)
-                0.0,  # 12: Left_Hip_Roll (lle2)
-                1.0,  # 13: Left_Hip_Yaw (lle3)
-                0.0,  # 14: Left_Knee_Pitch (lle4)
-                0.0,  # 15: Left_Ankle_Pitch (lle5)
-                0.0,  # 16: Left_Ankle_Roll (lle6)
-                0.0,  # 17: Right_Hip_Pitch (rle1)
-                0.0,  # 18: Right_Hip_Roll (rle2)
-                1.0,  # 19: Right_Hip_Yaw (rle3)
-                0.0,  # 20: Right_Knee_Pitch (rle4)
-                0.0,  # 21: Right_Ankle_Pitch (rle5)
-                0.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-        self.joint_nominal_position = np.zeros(self.no_of_actions)
-        self.train_sim_flip = np.array(
-            [
-                1.0,  # 0: Head_yaw (he1)
-                -1.0,  # 1: Head_pitch (he2)
-                1.0,  # 2: Left_Shoulder_Pitch (lae1)
-                -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                -1.0,  # 4: Left_Elbow_Pitch (lae3)
-                1.0,  # 5: Left_Elbow_Yaw (lae4)
-                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                -1.0,  # 7: Right_Shoulder_Roll (rae2)
-                1.0,  # 8: Right_Elbow_Pitch (rae3)
-                1.0,  # 9: Right_Elbow_Yaw (rae4)
-                1.0,  # 10: Waist (te1)
-                1.0,  # 11: Left_Hip_Pitch (lle1)
-                -1.0,  # 12: Left_Hip_Roll (lle2)
-                -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,  # 14: Left_Knee_Pitch (lle4)
-                1.0,  # 15: Left_Ankle_Pitch (lle5)
-                -1.0,  # 16: Left_Ankle_Roll (lle6)
-                -1.0,  # 17: Right_Hip_Pitch (rle1)
-                -1.0,  # 18: Right_Hip_Roll (rle2)
-                -1.0,  # 19: Right_Hip_Yaw (rle3)
-                -1.0,  # 20: Right_Knee_Pitch (rle4)
-                -1.0,  # 21: Right_Ankle_Pitch (rle5)
-                -1.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-
-        self.scaling_factor = 0.3
-        # self.scaling_factor = 1
-
-        # Encourage a minimum lateral stance so the policy avoids feet overlap.
-        self.min_stance_rad = 0.10
-
-        # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = False
-        self.reset_beam_yaw_range_deg = float(os.environ.get("GYM_CPU_RESET_BEAM_YAW_RANGE_DEG", "180"))
-        self.reset_target_bearing_range_deg = float(os.environ.get("GYM_CPU_RESET_TARGET_BEARING_RANGE_DEG", "45"))
-        self.reset_target_distance_min = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MIN", "1.2"))
-        self.reset_target_distance_max = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MAX", "2.8"))
-        if self.reset_target_distance_min > self.reset_target_distance_max:
-            self.reset_target_distance_min, self.reset_target_distance_max = (
-                self.reset_target_distance_max,
-                self.reset_target_distance_min,
-            )
-        self.reset_joint_noise_rad = 0.025
-        self.reset_perturb_steps = 4
-        self.reset_recover_steps = 8
-
-        self.reward_smoothness_scale = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_SCALE", "0.06"))
-        self.reward_smoothness_cap = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_CAP", "0.45"))
-        self.reward_head_toward_bonus = float(os.environ.get("GYM_CPU_REWARD_HEAD_TOWARD_BONUS", "1"))
-
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Track previous position
-        self.last_yaw_error = None
-        self.Player.server.connect()
-        # sleep(2.0)  # Longer wait for connection to establish completely
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-        self.start_time = time.time()
-
-    def _reconnect_server(self):
-        try:
-            self.Player.server.shutdown()
-        except Exception:
-            pass
-
-        self.Player.server.connect()
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-
-    def _safe_receive_world_update(self, retries=1):
-        last_exc = None
-        for attempt in range(retries + 1):
-            try:
-                self.Player.server.receive()
-                self.Player.world.update()
-                return
-            except (ConnectionResetError, OSError) as exc:
-                last_exc = exc
-                if attempt >= retries:
-                    raise
-                self._reconnect_server()
-        if last_exc is not None:
-            raise last_exc
-
-    def debug_log(self, message):
-        print(message)
-        try:
-            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
-            with open(log_path, "a", encoding="utf-8") as f:
-                f.write(message + "\n")
-        except OSError:
-            pass
-
-    @staticmethod
-    def _wrap_to_pi(angle_rad: float) -> float:
-        return (angle_rad + math.pi) % (2.0 * math.pi) - math.pi
-
-    def observe(self, init=False):
-
-        """获取当前观测值"""
-        robot = self.Player.robot
-        world = self.Player.world
-
-        # Safety check: ensure data is available
-
-        # 计算目标速度
-        raw_target = self.target_position - world.global_position[:2]
-        velocity = MathOps.rotate_2d_vec(
-            raw_target,
-            -robot.global_orientation_euler[2],
-            is_rad=False
-        )
-
-        # 计算相对方向
-        rel_orientation = MathOps.vector_angle(velocity) * 0.3
-        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
-
-        velocity = np.concatenate([velocity, np.array([rel_orientation])])
-        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
-        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
-
-        # 关节状态
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        qpos_qvel_previous_action = np.concatenate([
-            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
-            radian_joint_speeds / 110.0 * self.train_sim_flip,
-            self.previous_action / 10.0,
-        ])
-
-        # 角速度
-        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
-
-        # 投影的重力方向
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-
-        # 组合观测
-        observation = np.concatenate([
-            qpos_qvel_previous_action,
-            ang_vel,
-            velocity,
-            projected_gravity,
-        ])
-
-        observation = np.clip(observation, -10.0, 10.0)
-        return observation.astype(np.float32)
-
-    def sync(self):
-        ''' Run a single simulation step '''
-        self._safe_receive_world_update(retries=1)
-        self.Player.robot.commit_motor_targets_pd()
-        self.Player.server.send()
-        if self._target_dt > 0.0:
-            now = time.time()
-            if self._last_sync_time is None:
-                self._last_sync_time = now
-                return
-            elapsed = now - self._last_sync_time
-            remaining = self._target_dt - elapsed
-            if remaining > 0.0:
-                time.sleep(remaining)
-                now = time.time()
-            self._last_sync_time = now
-
-    def debug_joint_status(self):
-        robot = self.Player.robot
-        actual_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        target_joint_positions = getattr(
-            self,
-            'target_joint_positions',
-            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
-        )
-        joint_error = actual_joint_positions - target_joint_positions
-        leg_slice = slice(11, None)
-
-        self.debug_log(
-            "[WalkDebug] "
-            f"step={self.step_counter} "
-            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
-            f"target_xy={np.round(self.target_position, 3).tolist()} "
-            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
-            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
-            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
-            f"fallen={self.Player.world.global_position[2] < 0.3}"
-        )
-        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
-
-    def reset(self, seed=None, options=None):
-        '''
-        Reset and stabilize the robot
-        Note: for some behaviors it would be better to reduce stabilization or add noise
-        '''
-        r = self.Player.robot
-        super().reset(seed=seed)
-        if seed is not None:
-            np.random.seed(seed)
-
-        target_distance = np.random.uniform(self.reset_target_distance_min, self.reset_target_distance_max)
-        target_bearing_deg = np.random.uniform(-self.reset_target_bearing_range_deg, self.reset_target_bearing_range_deg)
-
-        self.step_counter = 0
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
-        self.last_yaw_error = None
-        self.walk_cycle_step = 0
-        self._reward_debug_steps_left = 0
-
-        # 随机 beam 目标位置和朝向，增加训练多样性
-        beam_x = (random() - 0.5) * 10
-        beam_y = (random() - 0.5) * 10
-        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
-
-        for _ in range(5):
-            self._safe_receive_world_update(retries=2)
-            self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
-            self.Player.server.send()
-
-        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
-        finished_count = 0
-        for _ in range(50):
-            finished = self.Player.skills_manager.execute("Neutral")
-            self.sync()
-            if finished:
-                finished_count += 1
-                if finished_count >= 20:  # 假设需要连续20次完成才算成功
-                    break
-
-        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-            # Perturb waist + lower body only (10:), keep head/arms stable.
-            perturb_action[10:] = np.random.uniform(
-                -self.reset_joint_noise_rad,
-                self.reset_joint_noise_rad,
-                size=(self.no_of_actions - 10,)
-            )
-
-            for _ in range(self.reset_perturb_steps):
-                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-            for i in range(self.reset_recover_steps):
-                # Linearly fade perturbation to help policy start from near-neutral.
-                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-        # memory variables
-        self.sync()  
-        self.initial_position = np.array(self.Player.world.global_position[:2])
-        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions, np.float32)
-        # Randomize global target bearing so policy must learn to rotate toward it first.
-        heading_deg = float(r.global_orientation_euler[2])
-        target_offset = MathOps.rotate_2d_vec(
-            np.array([target_distance, 0.0]),
-            heading_deg + target_bearing_deg,
-            is_rad=False,
-        )
-        point1 = self.initial_position + target_offset
-        self.point_list = [point1]
-        self.target_position = self.point_list[self.waypoint_index]
-        self.initial_height = self.Player.world.global_position[2]
-
-        return self.observe(True), {}
-
-    def render(self, mode='human', close=False):
-        return
-
-    
-    def compute_reward(self, previous_pos, current_pos, action):
-        height = float(self.Player.world.global_position[2])
-        robot = self.Player.robot
-
-
-        joint_pos_rad = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        joint_speed_rad = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-        tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        ang_vel = np.deg2rad(robot.gyroscope)
-        rp_ang_vel_mag = float(np.linalg.norm(ang_vel[:2]))
-
-        is_fallen = height < 0.55
-        if is_fallen:
-            # remain = max(0, 800 - self.step_counter)
-            # return -8.0 - 0.01 * remain
-            return -20.0
-        
-
-        if np.linalg.norm(current_pos - previous_pos) > 0.005:
-            position_penalty = -3 * float(np.linalg.norm(current_pos - previous_pos))
-        else:
-            position_penalty = 0.0
-
-
-        # Turn-to-target shaping.
-        to_target = self.target_position - current_pos
-        dist_to_target = float(np.linalg.norm(to_target))
-        if dist_to_target > 1e-6:
-            target_yaw = math.atan2(float(to_target[1]), float(to_target[0]))
-        else:
-            target_yaw = 0.0
-
-        robot_yaw = math.radians(float(robot.global_orientation_euler[2]))
-        yaw_error = target_yaw - robot_yaw
-
-        # Main heading objective: face the target direction.
-        # heading_align_reward = 1.0 * math.cos(yaw_error)
-
-        abs_yaw_error = abs(yaw_error)
-        alive_bonus = 2.0 * max(0.0, 1.0 - abs_yaw_error / math.pi)
-        head_toward_bonus = self.reward_head_toward_bonus if abs_yaw_error < math.radians(4.0) else 0.0
-
-        if self.last_yaw_error is None:
-            heading_progress_reward = 0.0
-        else:
-            prev_abs_yaw_error = abs(self.last_yaw_error)
-            yaw_err_delta = prev_abs_yaw_error - abs_yaw_error
-            progress_gate = 1.0 if abs_yaw_error > math.radians(4.0) else 0.0
-            heading_progress_reward =  0.8 * progress_gate * yaw_err_delta
-            heading_progress_reward = float(np.clip(heading_progress_reward, -0.4, 0.4))
-        self.last_yaw_error = yaw_error
-
-        # action_penalty = -0.01 * float(np.linalg.norm(action))
-        smoothness_penalty = -0.05 * float(np.linalg.norm(action - self.last_action_for_reward))
-
-        posture_penalty = -0.6 * tilt_mag
-        # Penalize roll/pitch rotational shake but do not penalize yaw turning directly.
-        ang_vel_penalty = -0.06 * rp_ang_vel_mag
-
-        joint_pos = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        ) * self.train_sim_flip
-        left_hip_roll = float(joint_pos[12])
-        right_hip_roll = float(joint_pos[18])
-        left_hip_pitch = float(joint_pos[11])
-        right_hip_pitch = float(joint_pos[17])
-
-        left_ankle_roll = float(joint_pos[16])
-        right_ankle_roll = float(joint_pos[22])
-
-        max_leg_roll = 0.15  # 防止劈叉姿势
-        split_penalty = -0.8 * max(0.0, (-left_hip_roll + right_hip_roll - 2 * max_leg_roll) / max_leg_roll)
-        left_hip_yaw = float(joint_pos[13])
-        right_hip_yaw = float(joint_pos[19])
-
-        min_leg_separation = 0.05  # 最小腿间距（防止贴得太近）
-        # 惩罚腿过分靠拢（内收）- 基于两腿间距
-        leg_separation = -left_hip_roll + right_hip_roll
-        inward_penalty = -0.25 * max(0.0, (min_leg_separation - leg_separation) / min_leg_separation)
-        
-        
-        # 脚踝roll角度检测：防止过度外翻或内翻
-        max_ankle_roll = 0.15  # 最大允许的脚踝roll角度
-        
-        # 惩罚脚踝过度外翻/内翻（绝对值过大）
-        ankle_roll_penalty = -0.5 * max(0.0, (abs(left_ankle_roll) + abs(right_ankle_roll) - 2 * max_ankle_roll) / max_ankle_roll)
-        
-        # 惩罚两脚踝roll方向相反（不稳定姿势）
-        ankle_roll_cross_penalty = -0.3 * max(0.0, -(left_ankle_roll * right_ankle_roll))
-
-       # 分别惩罚左右大腿过度转动
-        max_hip_yaw = 0.3  # 最大允许的yaw角度
-        left_hip_yaw_penalty = -0.4 * max(0.0, abs(left_hip_yaw) - max_hip_yaw)
-        right_hip_yaw_penalty = -0.4 * max(0.0, abs(right_hip_yaw) - max_hip_yaw)
-        # 智能交叉腿惩罚：只在站立时惩罚，转身时允许交叉腿
-        yaw_rate = float(np.deg2rad(robot.gyroscope[2]))
-        yaw_rate_abs = abs(yaw_rate)
-
-        # 当转身速度较小时才惩罚交叉腿（站立状态）
-        cross_leg_gate = max(0.0, 1.0 - yaw_rate_abs / math.radians(8.0))
-        hip_yaw_cross_penalty = -1.0 * cross_leg_gate * max(0.0, -(left_hip_yaw * right_hip_yaw)) if left_hip_yaw > 0 and right_hip_yaw < 0 else 0.0
-
-        # Torso-lower-body linkage: reward coordinated turning, punish waist-only spinning.
-        waist_speed = abs(float(joint_speed_rad[10]))
-        lower_body_speed = float(np.mean(np.abs(joint_speed_rad[11:23])))
-        lower_body_follow_ratio = lower_body_speed / (waist_speed + 1e-4)
-        linkage_reward = 0.24 * min(1.0, lower_body_follow_ratio) * min(1.0, waist_speed / 1.2)
-        waist_only_turn_penalty = -0.20 * max(0.0, waist_speed - 1.35 * lower_body_speed)
-
-        # Extra posture linkage in yaw joints to avoid decoupled torso twist.
-        waist_yaw = abs(float(joint_pos_rad[10]))
-        hip_yaw_mean = 0.5 * (abs(float(joint_pos_rad[13])) + abs(float(joint_pos_rad[19])))
-        yaw_link_reward = 0.12 * math.exp(-abs(waist_yaw - hip_yaw_mean) / 0.22)
-
-        target_height = self.initial_height
-        height_error =  height - target_height
-        height_error = height - target_height
-        
-        height_penalty = -(math.exp(12*abs(height_error))-1) if height_error > 0.04 else 0
-
-        # # 在 compute_reward 开头附近，添加高度变化率计算
-        # if not hasattr(self, 'last_height'):
-        #     self.last_height = height
-        #     self.last_height_time = self.step_counter  # 可选，用于时间间隔
-        # height_rate = height - self.last_height  # 正为上升，负为下降
-        # self.last_height = height
-
-        # 惩罚高度下降（负变化率）
-        # height_down_penalty = -5.0 * max(0, -height_rate)  # 系数可调，-height_rate 为正表示下降幅度
-
-        # # 在 compute_reward 中
-        # if self.step_counter > 50:
-        #     avg_prev_action = np.mean(self.prev_action_history, axis=0)
-        #     novelty = float(np.linalg.norm(action - avg_prev_action))
-        #     exploration_bonus = 0.05 * novelty
-        # else:
-        #     exploration_bonus = 0
-
-        # self.prev_action_history[self.history_idx] = action
-        # self.history_idx = (self.history_idx + 1) % 50
-
-
-        total = (
-            # progress_reward  + 
-                alive_bonus + 
-                head_toward_bonus +
-                heading_progress_reward +
-                # lateral_penalty +
-                # action_penalty +
-                smoothness_penalty +
-                posture_penalty
-                + ang_vel_penalty
-                + height_penalty
-                + ankle_roll_penalty
-                + ankle_roll_cross_penalty
-                + split_penalty
-                + inward_penalty
-                # + leg_proximity_penalty
-                + left_hip_yaw_penalty
-                + right_hip_yaw_penalty
-                + hip_yaw_cross_penalty
-                + position_penalty
-                # + linkage_reward
-                # + waist_only_turn_penalty
-                # + yaw_link_reward
-                # + stance_collapse_penalty
-                # + hip_yaw_yaw_cross_penalty
-                # + stance_collapse_penalty
-                #  + cross_leg_penalty
-                #  + exploration_bonus
-                # + height_down_penalty
-                 )
-        # print(height_error, height_penalty)
-        
-        now = time.time()
-        if self.reward_debug_interval_sec > 0 and now - self._reward_debug_last_time >= self.reward_debug_interval_sec:
-            self._reward_debug_last_time = now
-            self._reward_debug_steps_left = max(1, self.reward_debug_burst_steps)
-
-        if self._reward_debug_steps_left > 0:
-            self._reward_debug_steps_left -= 1
-            self.debug_log(
-                f"height_penalty:{height_penalty:.4f},"
-                f"smoothness_penalty:{smoothness_penalty:.4f},"
-                f"posture_penalty:{posture_penalty:.4f},"
-                f"heading_progress_reward:{heading_progress_reward:.4f},"
-                # f"stance_collapse_penalty:{stance_collapse_penalty:.4f},"
-                # f"cross_leg_penalty:{cross_leg_penalty:.4f},"
-                f"ang_vel_penalty:{ang_vel_penalty:.4f},"
-                f"split_penalty:{split_penalty:.4f},"
-                f"ankle_roll_penalty:{ankle_roll_penalty:.4f},"
-                f"ankle_roll_cross_penalty:{ankle_roll_cross_penalty:.4f},"
-                f"left_hip_yaw_penalty:{left_hip_yaw_penalty:.4f},"
-                f"right_hip_yaw_penalty:{right_hip_yaw_penalty:.4f},"
-                f"hip_yaw_cross_penalty:{hip_yaw_cross_penalty:.4f},"
-                f"inward_penalty:{inward_penalty:.4f},"
-                f"position_penalty:{position_penalty:.4f},"
-                # f"linkage_reward:{linkage_reward:.4f},"
-                # f"waist_only_turn_penalty:{waist_only_turn_penalty:.4f},"
-                # f"yaw_link_reward:{yaw_link_reward:.4f}"
-                # f"leg_proximity_penalty:{leg_proximity_penalty:.4f},"
-
-                # f"stance_collapse_penalty:{stance_collapse_penalty:.4f},"
-                # f"hip_yaw_yaw_cross_penalty:{hip_yaw_yaw_cross_penalty:.4f},"
-                #   f"height_down_penalty:{height_down_penalty:.4f}",
-                #   f"exploration_bonus:{exploration_bonus:.4f}"
-                f"alive_bonus:{alive_bonus:.4f},"
-                f"abs_yaw_error:{abs_yaw_error:.4f}"
-                f"total:{total:.4f}"
-                )
-
-        return total
-
-
-
-    def step(self, action):
-
-        r = self.Player.robot
-        max_action_delta =  0.5# Limit how much the action can change from the previous step to encourage smoother motions.
-        if self.previous_action is not None:
-            action = np.clip(action, self.previous_action - max_action_delta, self.previous_action + max_action_delta)
-        action[0:2] = 0
-        action[3] = 4
-        action[7] = -4
-        action[2] = 0
-        action[6] = 0
-        action[4] = 0
-        action[5] = -5
-        action[8] = 0
-        action[9] = 5
-        action[10] = 0
-        action[11] = np.clip(action[11], -0.1, 0.1)
-        action[17] = np.clip(action[17], -0.1, 0.1)
-        # action[12] = -1.0
-        # action[18] = 1.0
-        # action[13] = -1.0
-        # action[19] = 1.0
-        self.previous_action = action.copy()
-
-        self.target_joint_positions = (
-                # self.joint_nominal_position +
-                 self.scaling_factor * action
-        )
-        self.target_joint_positions *= self.train_sim_flip
-
-        for idx, target in enumerate(self.target_joint_positions):
-            r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=110, kd=29.5
-            )
-
-        self.previous_action = action.copy()
-
-        self.sync()  # run simulation step
-        self.step_counter += 1
-
-        if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
-            self.debug_joint_status()
-
-        current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
-
-        if self.step_counter % 10 == 0:
-            self.previous_pos = current_pos.copy()
-
-        # Compute reward based on movement from previous step
-        reward = self.compute_reward(self.previous_pos, current_pos, action)
-
-        self.last_action_for_reward = action.copy()
-
-        # Fall detection and penalty
-        is_fallen = self.Player.world.global_position[2] < 0.55
-
-        # terminal state: the robot is falling or timeout
-        terminated = is_fallen or self.step_counter > 800 or self.route_completed
-        truncated = False
-
-        return self.observe(), reward, terminated, truncated, {}
-
-
-class Train(Train_Base):
-    def __init__(self, script) -> None:
-        super().__init__(script)
-
-    def train(self, args):
-
-        # --------------------------------------- Learning parameters
-        n_envs = int(os.environ.get("GYM_CPU_N_ENVS", "20"))
-        if n_envs < 1:
-            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
-        server_warmup_sec = float(os.environ.get("GYM_CPU_SERVER_WARMUP_SEC", "3.0"))
-        n_steps_per_env = int(os.environ.get("GYM_CPU_TRAIN_STEPS_PER_ENV", "512"))  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = int(os.environ.get("GYM_CPU_TRAIN_BATCH_SIZE", "512"))  # should be a factor of (n_steps_per_env * n_envs)
-        total_steps = 30000000
-        learning_rate = float(os.environ.get("GYM_CPU_TRAIN_LR", "3e-4"))
-        folder_name = f'Turn_R{self.robot_type}'
-        model_path = f'./scripts/gyms/logs/{folder_name}/'
-
-        print(f"Model path: {model_path}")
-        print(f"Using {n_envs} parallel environments")
-
-        # --------------------------------------- Run algorithm
-        def init_env(i_env, monitor=False):
-            def thunk():
-                env = WalkEnv(self.ip, self.server_p + i_env)
-                if monitor:
-                    env = Monitor(env)
-                return env
-
-            return thunk
-
-        server_log_dir = os.path.join(model_path, "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1, no_render=True, no_realtime=True)  # include 1 extra server for testing
-
-        # Wait for servers to start
-        print(f"Starting {n_envs + 1} rcssservermj servers...")
-        if server_warmup_sec > 0:
-            print(f"Waiting {server_warmup_sec:.1f}s for server warmup...")
-            sleep(server_warmup_sec)
-        print("Servers started, creating environments...")
-
-        env = SubprocVecEnv([init_env(i, monitor=True) for i in range(n_envs)], start_method="spawn")
-        # Use single-process eval env to avoid extra subprocess fragility during callback evaluation.
-        eval_env = DummyVecEnv([init_env(n_envs, monitor=True)])
-
-        try:
-            # Custom policy network architecture
-            policy_kwargs = dict(
-                net_arch=dict(
-                    pi=[512, 256, 128],  # Policy network: 3 layers
-                    vf=[512, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
-            )
-
-            if "model_file" in args:  # retrain
-                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
-                                 batch_size=minibatch_size, learning_rate=learning_rate)
-            else:  # train new model
-                model = PPO(
-                    "MlpPolicy",
-                    env=env,
-                    verbose=1,
-                    n_steps=n_steps_per_env,
-                    batch_size=minibatch_size,
-                    learning_rate=learning_rate,
-                    device="cpu",
-                    policy_kwargs=policy_kwargs,
-                    ent_coef=float(os.environ.get("GYM_CPU_TRAIN_ENT_COEF", "0.05")),  # Entropy coefficient for exploration
-                    clip_range=float(os.environ.get("GYM_CPU_TRAIN_CLIP_RANGE", "0.2")),  # PPO clipping parameter
-                    gae_lambda=0.95,  # GAE lambda
-                    gamma=float(os.environ.get("GYM_CPU_TRAIN_GAMMA", "0.95")), # Discount factor
-                    # target_kl=0.03,
-                    n_epochs=int(os.environ.get("GYM_CPU_TRAIN_EPOCHS", "5")),
-                    tensorboard_log=f"./scripts/gyms/logs/{folder_name}/tensorboard/"
-                )
-
-            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
-                                          eval_freq=n_steps_per_env * 20, save_freq=n_steps_per_env * 20, eval_eps=7,
-                                          backup_env_file=__file__)
-        except KeyboardInterrupt:
-            sleep(1)  # wait for child processes
-            print("\nctrl+c pressed, aborting...\n")
-            servers.kill()
-            return
-
-        env.close()
-        eval_env.close()
-        servers.kill()
-
-    def test(self, args):
-
-        # Uses different server and monitor ports
-        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        test_no_render = os.environ.get("GYM_CPU_TEST_NO_RENDER", "0") == "1"
-        test_no_realtime = os.environ.get("GYM_CPU_TEST_NO_REALTIME", "0") == "1"
-
-        server = Train_Server(
-            self.server_p - 1,
-            self.monitor_p,
-            1,
-            no_render=test_no_render,
-            no_realtime=test_no_realtime,
-        )
-        env = WalkEnv(self.ip, self.server_p - 1)
-        model = PPO.load(args["model_file"], env=env)
-
-        try:
-            self.export_model(args["model_file"], args["model_file"] + ".pkl",
-                              False)  # Export to pkl to create custom behavior
-            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
-        except KeyboardInterrupt:
-            print()
-
-        env.close()
-        server.kill()
-
-
-if __name__ == "__main__":
-    from types import SimpleNamespace
-
-    # 创建默认参数
-    script_args = SimpleNamespace(
-        args=SimpleNamespace(
-            i='127.0.0.1',  # Server IP
-            p=3100,  # Server port
-            m=3200,  # Monitor port
-            r=0,  # Robot type
-            t='Gym',  # Team name
-            u=1  # Uniform number
-        )
-    )
-
-    trainer = Train(script_args)
-
-    run_mode = os.environ.get("GYM_CPU_MODE", "train").strip().lower()
-
-    if run_mode == "test":
-        test_model_file = os.environ.get("GYM_CPU_TEST_MODEL", "scripts/gyms/logs/Turn_R0_004/best_model.zip")
-        test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Turn_R0_004/")
-        trainer.test({"model_file": test_model_file, "folder_dir": test_folder})
-    else:
-        retrain_model = os.environ.get("GYM_CPU_TRAIN_MODEL", "").strip()
-        if retrain_model:
-            trainer.train({"model_file": retrain_model})
-        else:
-            trainer.train({})

scripts/gyms/logs/Turn_R0_011/Walk.py

@@ -1,853 +0,0 @@
-import os
-import numpy as np
-import math
-import time
-from time import sleep
-from random import random
-from random import uniform
-from itertools import count
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.monitor import Monitor
-from stable_baselines3.common.vec_env import SubprocVecEnv, DummyVecEnv
-
-import gymnasium as gym
-from gymnasium import spaces
-
-from scripts.commons.Train_Base import Train_Base
-from scripts.commons.Server import Server as Train_Server
-
-from agent.base_agent import Base_Agent
-from utils.math_ops import MathOps
-
-from scipy.spatial.transform import Rotation as R
-
-'''
-Objective:
-Learn how to run forward using step primitive
-----------
-- class Basic_Run: implements an OpenAI custom gym
-- class Train:  implements algorithms to train a new model or test an existing model
-'''
-
-
-class WalkEnv(gym.Env):
-    def __init__(self, ip, server_p) -> None:
-
-        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
-        self.Player = player = Base_Agent(
-            team_name="Gym",
-            number=1,
-            host=ip,
-            port=server_p
-        )
-        self.robot_type = self.Player.robot
-        self.step_counter = 0  # to limit episode size
-        self.force_play_on = True
-
-        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
-        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
-        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
-        self.isfallen = False
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.debug_every_n_steps = 5
-        self.enable_debug_joint_status = False
-        self.reward_debug_interval_sec = float(os.environ.get("GYM_CPU_REWARD_DEBUG_INTERVAL_SEC", "600"))
-        self.reward_debug_burst_steps = int(os.environ.get("GYM_CPU_REWARD_DEBUG_BURST_STEPS", "10"))
-        self._reward_debug_last_time = time.time()
-        self._reward_debug_steps_left = 0
-        self.calibrate_nominal_from_neutral = True
-        self.auto_calibrate_train_sim_flip = True
-        self.nominal_calibrated_once = False
-        self.flip_calibrated_once = False
-        self._target_hz = 0.0
-        self._target_dt = 0.0
-        self._last_sync_time = None
-        target_hz_env = 0
-        if target_hz_env:
-            try:
-                self._target_hz = float(target_hz_env)
-            except ValueError:
-                self._target_hz = 0.0
-        if self._target_hz > 0.0:
-            self._target_dt = 1.0 / self._target_hz
-
-        # State space
-        # 原始观测大小: 78
-        obs_size = 78
-        self.obs = np.zeros(obs_size, np.float32)
-        self.observation_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(obs_size,),
-            dtype=np.float32
-        )
-
-        action_dim = len(self.Player.robot.ROBOT_MOTORS)
-        self.no_of_actions = action_dim
-        self.action_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(action_dim,),
-            dtype=np.float32
-        )
-
-        # 中立姿态
-        self.joint_nominal_position = np.array(
-            [
-                0.0,  # 0: Head_yaw (he1)
-                0.0,  # 1: Head_pitch (he2)
-                0.0,  # 2: Left_Shoulder_Pitch (lae1)
-                0.0,  # 3: Left_Shoulder_Roll (lae2)
-                0.0,  # 4: Left_Elbow_Pitch (lae3)
-                0.0,  # 5: Left_Elbow_Yaw (lae4)
-                0.0,  # 6: Right_Shoulder_Pitch (rae1)
-                0.0,  # 7: Right_Shoulder_Roll (rae2)
-                0.0,  # 8: Right_Elbow_Pitch (rae3)
-                0.0,  # 9: Right_Elbow_Yaw (rae4)
-                0.0,  # 10: Waist (te1)
-                0.0,  # 11: Left_Hip_Pitch (lle1)
-                0.0,  # 12: Left_Hip_Roll (lle2)
-                1.0,  # 13: Left_Hip_Yaw (lle3)
-                0.0,  # 14: Left_Knee_Pitch (lle4)
-                0.0,  # 15: Left_Ankle_Pitch (lle5)
-                0.0,  # 16: Left_Ankle_Roll (lle6)
-                0.0,  # 17: Right_Hip_Pitch (rle1)
-                0.0,  # 18: Right_Hip_Roll (rle2)
-                1.0,  # 19: Right_Hip_Yaw (rle3)
-                0.0,  # 20: Right_Knee_Pitch (rle4)
-                0.0,  # 21: Right_Ankle_Pitch (rle5)
-                0.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-        self.joint_nominal_position = np.zeros(self.no_of_actions)
-        self.train_sim_flip = np.array(
-            [
-                1.0,  # 0: Head_yaw (he1)
-                -1.0,  # 1: Head_pitch (he2)
-                1.0,  # 2: Left_Shoulder_Pitch (lae1)
-                -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                -1.0,  # 4: Left_Elbow_Pitch (lae3)
-                1.0,  # 5: Left_Elbow_Yaw (lae4)
-                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                -1.0,  # 7: Right_Shoulder_Roll (rae2)
-                1.0,  # 8: Right_Elbow_Pitch (rae3)
-                1.0,  # 9: Right_Elbow_Yaw (rae4)
-                1.0,  # 10: Waist (te1)
-                1.0,  # 11: Left_Hip_Pitch (lle1)
-                -1.0,  # 12: Left_Hip_Roll (lle2)
-                -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,  # 14: Left_Knee_Pitch (lle4)
-                1.0,  # 15: Left_Ankle_Pitch (lle5)
-                -1.0,  # 16: Left_Ankle_Roll (lle6)
-                -1.0,  # 17: Right_Hip_Pitch (rle1)
-                -1.0,  # 18: Right_Hip_Roll (rle2)
-                -1.0,  # 19: Right_Hip_Yaw (rle3)
-                -1.0,  # 20: Right_Knee_Pitch (rle4)
-                -1.0,  # 21: Right_Ankle_Pitch (rle5)
-                -1.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-
-        self.scaling_factor = 0.3
-        # self.scaling_factor = 1
-
-        # Encourage a minimum lateral stance so the policy avoids feet overlap.
-        self.min_stance_rad = 0.10
-
-        # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = False
-        self.reset_beam_yaw_range_deg = float(os.environ.get("GYM_CPU_RESET_BEAM_YAW_RANGE_DEG", "180"))
-        self.reset_target_bearing_range_deg = float(os.environ.get("GYM_CPU_RESET_TARGET_BEARING_RANGE_DEG", "45"))
-        self.reset_target_distance_min = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MIN", "1.2"))
-        self.reset_target_distance_max = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MAX", "2.8"))
-        if self.reset_target_distance_min > self.reset_target_distance_max:
-            self.reset_target_distance_min, self.reset_target_distance_max = (
-                self.reset_target_distance_max,
-                self.reset_target_distance_min,
-            )
-        self.reset_joint_noise_rad = 0.025
-        self.reset_perturb_steps = 4
-        self.reset_recover_steps = 8
-
-        self.reward_smoothness_scale = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_SCALE", "0.06"))
-        self.reward_smoothness_cap = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_CAP", "0.45"))
-        self.reward_head_toward_bonus = float(os.environ.get("GYM_CPU_REWARD_HEAD_TOWARD_BONUS", "1"))
-
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Track previous position
-        self.last_yaw_error = None
-        self.Player.server.connect()
-        # sleep(2.0)  # Longer wait for connection to establish completely
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-        self.start_time = time.time()
-
-    def _reconnect_server(self):
-        try:
-            self.Player.server.shutdown()
-        except Exception:
-            pass
-
-        self.Player.server.connect()
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-
-    def _safe_receive_world_update(self, retries=1):
-        last_exc = None
-        for attempt in range(retries + 1):
-            try:
-                self.Player.server.receive()
-                self.Player.world.update()
-                return
-            except (ConnectionResetError, OSError) as exc:
-                last_exc = exc
-                if attempt >= retries:
-                    raise
-                self._reconnect_server()
-        if last_exc is not None:
-            raise last_exc
-
-    def debug_log(self, message):
-        print(message)
-        try:
-            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
-            with open(log_path, "a", encoding="utf-8") as f:
-                f.write(message + "\n")
-        except OSError:
-            pass
-
-    @staticmethod
-    def _wrap_to_pi(angle_rad: float) -> float:
-        return (angle_rad + math.pi) % (2.0 * math.pi) - math.pi
-
-    def observe(self, init=False):
-
-        """获取当前观测值"""
-        robot = self.Player.robot
-        world = self.Player.world
-
-        # Safety check: ensure data is available
-
-        # 计算目标速度
-        raw_target = self.target_position - world.global_position[:2]
-        velocity = MathOps.rotate_2d_vec(
-            raw_target,
-            -robot.global_orientation_euler[2],
-            is_rad=False
-        )
-
-        # 计算相对方向
-        rel_orientation = MathOps.vector_angle(velocity) * 0.3
-        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
-
-        velocity = np.concatenate([velocity, np.array([rel_orientation])])
-        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
-        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
-
-        # 关节状态
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        qpos_qvel_previous_action = np.concatenate([
-            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
-            radian_joint_speeds / 110.0 * self.train_sim_flip,
-            self.previous_action / 10.0,
-        ])
-
-        # 角速度
-        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
-
-        # 投影的重力方向
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-
-        # 组合观测
-        observation = np.concatenate([
-            qpos_qvel_previous_action,
-            ang_vel,
-            velocity,
-            projected_gravity,
-        ])
-
-        observation = np.clip(observation, -10.0, 10.0)
-        return observation.astype(np.float32)
-
-    def sync(self):
-        ''' Run a single simulation step '''
-        self._safe_receive_world_update(retries=1)
-        self.Player.robot.commit_motor_targets_pd()
-        self.Player.server.send()
-        if self._target_dt > 0.0:
-            now = time.time()
-            if self._last_sync_time is None:
-                self._last_sync_time = now
-                return
-            elapsed = now - self._last_sync_time
-            remaining = self._target_dt - elapsed
-            if remaining > 0.0:
-                time.sleep(remaining)
-                now = time.time()
-            self._last_sync_time = now
-
-    def debug_joint_status(self):
-        robot = self.Player.robot
-        actual_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        target_joint_positions = getattr(
-            self,
-            'target_joint_positions',
-            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
-        )
-        joint_error = actual_joint_positions - target_joint_positions
-        leg_slice = slice(11, None)
-
-        self.debug_log(
-            "[WalkDebug] "
-            f"step={self.step_counter} "
-            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
-            f"target_xy={np.round(self.target_position, 3).tolist()} "
-            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
-            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
-            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
-            f"fallen={self.Player.world.global_position[2] < 0.3}"
-        )
-        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
-
-    def reset(self, seed=None, options=None):
-        '''
-        Reset and stabilize the robot
-        Note: for some behaviors it would be better to reduce stabilization or add noise
-        '''
-        r = self.Player.robot
-        super().reset(seed=seed)
-        if seed is not None:
-            np.random.seed(seed)
-
-        target_distance = np.random.uniform(self.reset_target_distance_min, self.reset_target_distance_max)
-        target_bearing_deg = np.random.uniform(-self.reset_target_bearing_range_deg, self.reset_target_bearing_range_deg)
-
-        self.step_counter = 0
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
-        self.last_yaw_error = None
-        self.walk_cycle_step = 0
-        self._reward_debug_steps_left = 0
-
-        # 随机 beam 目标位置和朝向，增加训练多样性
-        beam_x = (random() - 0.5) * 10
-        beam_y = (random() - 0.5) * 10
-        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
-
-        for _ in range(5):
-            self._safe_receive_world_update(retries=2)
-            self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
-            self.Player.server.send()
-
-        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
-        finished_count = 0
-        for _ in range(50):
-            finished = self.Player.skills_manager.execute("Neutral")
-            self.sync()
-            if finished:
-                finished_count += 1
-                if finished_count >= 20:  # 假设需要连续20次完成才算成功
-                    break
-
-        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-            # Perturb waist + lower body only (10:), keep head/arms stable.
-            perturb_action[10:] = np.random.uniform(
-                -self.reset_joint_noise_rad,
-                self.reset_joint_noise_rad,
-                size=(self.no_of_actions - 10,)
-            )
-
-            for _ in range(self.reset_perturb_steps):
-                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-            for i in range(self.reset_recover_steps):
-                # Linearly fade perturbation to help policy start from near-neutral.
-                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-        # memory variables
-        self.sync()  
-        self.initial_position = np.array(self.Player.world.global_position[:2])
-        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions, np.float32)
-        # Randomize global target bearing so policy must learn to rotate toward it first.
-        heading_deg = float(r.global_orientation_euler[2])
-        target_offset = MathOps.rotate_2d_vec(
-            np.array([target_distance, 0.0]),
-            heading_deg + target_bearing_deg,
-            is_rad=False,
-        )
-        point1 = self.initial_position + target_offset
-        self.point_list = [point1]
-        self.target_position = self.point_list[self.waypoint_index]
-        self.initial_height = self.Player.world.global_position[2]
-
-        return self.observe(True), {}
-
-    def render(self, mode='human', close=False):
-        return
-
-    
-    def compute_reward(self, previous_pos, current_pos, action):
-        height = float(self.Player.world.global_position[2])
-        robot = self.Player.robot
-
-
-        joint_pos_rad = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        joint_speed_rad = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-        tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        ang_vel = np.deg2rad(robot.gyroscope)
-        rp_ang_vel_mag = float(np.linalg.norm(ang_vel[:2]))
-
-        is_fallen = height < 0.55
-        if is_fallen:
-            # remain = max(0, 800 - self.step_counter)
-            # return -8.0 - 0.01 * remain
-            return -20.0
-        
-
-        if np.linalg.norm(current_pos - previous_pos) > 0.005:
-            position_penalty = -3 * float(np.linalg.norm(current_pos - previous_pos))
-        else:
-            position_penalty = 0.0
-
-
-        # Turn-to-target shaping.
-        to_target = self.target_position - current_pos
-        dist_to_target = float(np.linalg.norm(to_target))
-        if dist_to_target > 1e-6:
-            target_yaw = math.atan2(float(to_target[1]), float(to_target[0]))
-        else:
-            target_yaw = 0.0
-
-        robot_yaw = math.radians(float(robot.global_orientation_euler[2]))
-        yaw_error = target_yaw - robot_yaw
-
-        # Main heading objective: face the target direction.
-        # heading_align_reward = 1.0 * math.cos(yaw_error)
-
-        abs_yaw_error = abs(yaw_error)
-        alive_bonus = 2.0 * max(0.0, 1.0 - abs_yaw_error / math.pi)
-        head_toward_bonus = self.reward_head_toward_bonus if abs_yaw_error < math.radians(4.0) else 0.0
-
-        if self.last_yaw_error is None:
-            heading_progress_reward = 0.0
-        else:
-            prev_abs_yaw_error = abs(self.last_yaw_error)
-            yaw_err_delta = prev_abs_yaw_error - abs_yaw_error
-            progress_gate = 1.0 if abs_yaw_error > math.radians(4.0) else 0.0
-            heading_progress_reward =  0.8 * progress_gate * yaw_err_delta
-            heading_progress_reward = float(np.clip(heading_progress_reward, -0.4, 0.4))
-        self.last_yaw_error = yaw_error
-
-        # action_penalty = -0.01 * float(np.linalg.norm(action))
-        smoothness_penalty = -0.05 * float(np.linalg.norm(action - self.last_action_for_reward))
-
-        posture_penalty = -0.6 * tilt_mag
-        # Penalize roll/pitch rotational shake but do not penalize yaw turning directly.
-        ang_vel_penalty = -0.06 * rp_ang_vel_mag
-
-        joint_pos = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        ) * self.train_sim_flip
-        left_hip_roll = float(joint_pos[12])
-        right_hip_roll = float(joint_pos[18])
-        left_hip_pitch = float(joint_pos[11])
-        right_hip_pitch = float(joint_pos[17])
-
-        left_ankle_roll = float(joint_pos[16])
-        right_ankle_roll = float(joint_pos[22])
-
-        max_leg_roll = 0.15  # 防止劈叉姿势
-        split_penalty = -0.8 * max(0.0, (-left_hip_roll + right_hip_roll - 2 * max_leg_roll) / max_leg_roll)
-        left_hip_yaw = float(joint_pos[13])
-        right_hip_yaw = float(joint_pos[19])
-
-        min_leg_separation = 0.05  # 最小腿间距（防止贴得太近）
-        # 惩罚腿过分靠拢（内收）- 基于两腿间距
-        leg_separation = -left_hip_roll + right_hip_roll
-        inward_penalty = -0.25 * max(0.0, (min_leg_separation - leg_separation) / min_leg_separation)
-        
-        
-        # 脚踝roll角度检测：防止过度外翻或内翻
-        max_ankle_roll = 0.15  # 最大允许的脚踝roll角度
-        
-        # 惩罚脚踝过度外翻/内翻（绝对值过大）
-        ankle_roll_penalty = -0.5 * max(0.0, (abs(left_ankle_roll) + abs(right_ankle_roll) - 2 * max_ankle_roll) / max_ankle_roll)
-        
-        # 惩罚两脚踝roll方向相反（不稳定姿势）
-        ankle_roll_cross_penalty = -0.3 * max(0.0, -(left_ankle_roll * right_ankle_roll))
-
-       # 分别惩罚左右大腿过度转动
-        max_hip_yaw = 0.3  # 最大允许的yaw角度
-        left_hip_yaw_penalty = -0.4 * max(0.0, abs(left_hip_yaw) - max_hip_yaw)
-        right_hip_yaw_penalty = -0.4 * max(0.0, abs(right_hip_yaw) - max_hip_yaw)
-        # 智能交叉腿惩罚：只在站立时惩罚，转身时允许交叉腿
-        yaw_rate = float(np.deg2rad(robot.gyroscope[2]))
-        yaw_rate_abs = abs(yaw_rate)
-
-        # 当转身速度较小时才惩罚交叉腿（站立状态）
-        cross_leg_gate = max(0.0, 1.0 - yaw_rate_abs / math.radians(8.0))
-        hip_yaw_cross_penalty = -1.0 * cross_leg_gate * max(0.0, -(left_hip_yaw * right_hip_yaw)) if left_hip_yaw > 0 and right_hip_yaw < 0 else 0.0
-
-        # Torso-lower-body linkage: reward coordinated turning, punish waist-only spinning.
-        waist_speed = abs(float(joint_speed_rad[10]))
-        lower_body_speed = float(np.mean(np.abs(joint_speed_rad[11:23])))
-        lower_body_follow_ratio = lower_body_speed / (waist_speed + 1e-4)
-        linkage_reward = 0.24 * min(1.0, lower_body_follow_ratio) * min(1.0, waist_speed / 1.2)
-        waist_only_turn_penalty = -0.20 * max(0.0, waist_speed - 1.35 * lower_body_speed)
-
-        # Extra posture linkage in yaw joints to avoid decoupled torso twist.
-        waist_yaw = abs(float(joint_pos_rad[10]))
-        hip_yaw_mean = 0.5 * (abs(float(joint_pos_rad[13])) + abs(float(joint_pos_rad[19])))
-        yaw_link_reward = 0.12 * math.exp(-abs(waist_yaw - hip_yaw_mean) / 0.22)
-
-        target_height = self.initial_height
-        height_error =  height - target_height
-        height_error = height - target_height
-        
-        height_penalty = -(math.exp(12*abs(height_error))-1) if height_error > 0.04 else 0
-
-        # # 在 compute_reward 开头附近，添加高度变化率计算
-        # if not hasattr(self, 'last_height'):
-        #     self.last_height = height
-        #     self.last_height_time = self.step_counter  # 可选，用于时间间隔
-        # height_rate = height - self.last_height  # 正为上升，负为下降
-        # self.last_height = height
-
-        # 惩罚高度下降（负变化率）
-        # height_down_penalty = -5.0 * max(0, -height_rate)  # 系数可调，-height_rate 为正表示下降幅度
-
-        # # 在 compute_reward 中
-        # if self.step_counter > 50:
-        #     avg_prev_action = np.mean(self.prev_action_history, axis=0)
-        #     novelty = float(np.linalg.norm(action - avg_prev_action))
-        #     exploration_bonus = 0.05 * novelty
-        # else:
-        #     exploration_bonus = 0
-
-        # self.prev_action_history[self.history_idx] = action
-        # self.history_idx = (self.history_idx + 1) % 50
-
-
-        total = (
-            # progress_reward  + 
-                alive_bonus + 
-                head_toward_bonus +
-                heading_progress_reward +
-                # lateral_penalty +
-                # action_penalty +
-                smoothness_penalty +
-                posture_penalty
-                + ang_vel_penalty
-                + height_penalty
-                + ankle_roll_penalty
-                + ankle_roll_cross_penalty
-                + split_penalty
-                + inward_penalty
-                # + leg_proximity_penalty
-                + left_hip_yaw_penalty
-                + right_hip_yaw_penalty
-                + hip_yaw_cross_penalty
-                + position_penalty
-                # + linkage_reward
-                # + waist_only_turn_penalty
-                # + yaw_link_reward
-                # + stance_collapse_penalty
-                # + hip_yaw_yaw_cross_penalty
-                # + stance_collapse_penalty
-                #  + cross_leg_penalty
-                #  + exploration_bonus
-                # + height_down_penalty
-                 )
-        # print(height_error, height_penalty)
-        
-        now = time.time()
-        if self.reward_debug_interval_sec > 0 and now - self._reward_debug_last_time >= self.reward_debug_interval_sec:
-            self._reward_debug_last_time = now
-            self._reward_debug_steps_left = max(1, self.reward_debug_burst_steps)
-
-        if self._reward_debug_steps_left > 0:
-            self._reward_debug_steps_left -= 1
-            self.debug_log(
-                f"height_penalty:{height_penalty:.4f},"
-                f"smoothness_penalty:{smoothness_penalty:.4f},"
-                f"posture_penalty:{posture_penalty:.4f},"
-                f"heading_progress_reward:{heading_progress_reward:.4f},"
-                # f"stance_collapse_penalty:{stance_collapse_penalty:.4f},"
-                # f"cross_leg_penalty:{cross_leg_penalty:.4f},"
-                f"ang_vel_penalty:{ang_vel_penalty:.4f},"
-                f"split_penalty:{split_penalty:.4f},"
-                f"ankle_roll_penalty:{ankle_roll_penalty:.4f},"
-                f"ankle_roll_cross_penalty:{ankle_roll_cross_penalty:.4f},"
-                f"left_hip_yaw_penalty:{left_hip_yaw_penalty:.4f},"
-                f"right_hip_yaw_penalty:{right_hip_yaw_penalty:.4f},"
-                f"hip_yaw_cross_penalty:{hip_yaw_cross_penalty:.4f},"
-                f"inward_penalty:{inward_penalty:.4f},"
-                f"position_penalty:{position_penalty:.4f},"
-                # f"linkage_reward:{linkage_reward:.4f},"
-                # f"waist_only_turn_penalty:{waist_only_turn_penalty:.4f},"
-                # f"yaw_link_reward:{yaw_link_reward:.4f}"
-                # f"leg_proximity_penalty:{leg_proximity_penalty:.4f},"
-
-                # f"stance_collapse_penalty:{stance_collapse_penalty:.4f},"
-                # f"hip_yaw_yaw_cross_penalty:{hip_yaw_yaw_cross_penalty:.4f},"
-                #   f"height_down_penalty:{height_down_penalty:.4f}",
-                #   f"exploration_bonus:{exploration_bonus:.4f}"
-                f"alive_bonus:{alive_bonus:.4f},"
-                f"abs_yaw_error:{abs_yaw_error:.4f}"
-                f"total:{total:.4f}"
-                )
-
-        return total
-
-
-
-    def step(self, action):
-
-        r = self.Player.robot
-        max_action_delta =  0.5# Limit how much the action can change from the previous step to encourage smoother motions.
-        if self.previous_action is not None:
-            action = np.clip(action, self.previous_action - max_action_delta, self.previous_action + max_action_delta)
-        action[0:2] = 0
-        action[3] = 4
-        action[7] = -4
-        action[2] = 0
-        action[6] = 0
-        action[4] = 0
-        action[5] = -5
-        action[8] = 0
-        action[9] = 5
-        action[10] = 0
-        action[11] = np.clip(action[11], -0.1, 0.1)
-        action[17] = np.clip(action[17], -0.1, 0.1)
-        # action[12] = -1.0
-        # action[18] = 1.0
-        # action[13] = -1.0
-        # action[19] = 1.0
-        self.previous_action = action.copy()
-
-        self.target_joint_positions = (
-                # self.joint_nominal_position +
-                 self.scaling_factor * action
-        )
-        self.target_joint_positions *= self.train_sim_flip
-
-        for idx, target in enumerate(self.target_joint_positions):
-            r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=110, kd=6
-            )
-
-        self.previous_action = action.copy()
-
-        self.sync()  # run simulation step
-        self.step_counter += 1
-
-        if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
-            self.debug_joint_status()
-
-        current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
-
-        if self.step_counter % 10 == 0:
-            self.previous_pos = current_pos.copy()
-
-        # Compute reward based on movement from previous step
-        reward = self.compute_reward(self.previous_pos, current_pos, action)
-
-        self.last_action_for_reward = action.copy()
-
-        # Fall detection and penalty
-        is_fallen = self.Player.world.global_position[2] < 0.55
-
-        # terminal state: the robot is falling or timeout
-        terminated = is_fallen or self.step_counter > 800 or self.route_completed
-        truncated = False
-
-        return self.observe(), reward, terminated, truncated, {}
-
-
-class Train(Train_Base):
-    def __init__(self, script) -> None:
-        super().__init__(script)
-
-    def train(self, args):
-
-        # --------------------------------------- Learning parameters
-        n_envs = int(os.environ.get("GYM_CPU_N_ENVS", "20"))
-        if n_envs < 1:
-            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
-        server_warmup_sec = float(os.environ.get("GYM_CPU_SERVER_WARMUP_SEC", "3.0"))
-        n_steps_per_env = int(os.environ.get("GYM_CPU_TRAIN_STEPS_PER_ENV", "512"))  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = int(os.environ.get("GYM_CPU_TRAIN_BATCH_SIZE", "512"))  # should be a factor of (n_steps_per_env * n_envs)
-        total_steps = 30000000
-        learning_rate = float(os.environ.get("GYM_CPU_TRAIN_LR", "3e-4"))
-        folder_name = f'Turn_R{self.robot_type}'
-        model_path = f'./scripts/gyms/logs/{folder_name}/'
-
-        print(f"Model path: {model_path}")
-        print(f"Using {n_envs} parallel environments")
-
-        # --------------------------------------- Run algorithm
-        def init_env(i_env, monitor=False):
-            def thunk():
-                env = WalkEnv(self.ip, self.server_p + i_env)
-                if monitor:
-                    env = Monitor(env)
-                return env
-
-            return thunk
-
-        server_log_dir = os.path.join(model_path, "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1, no_render=True, no_realtime=True)  # include 1 extra server for testing
-
-        # Wait for servers to start
-        print(f"Starting {n_envs + 1} rcssservermj servers...")
-        if server_warmup_sec > 0:
-            print(f"Waiting {server_warmup_sec:.1f}s for server warmup...")
-            sleep(server_warmup_sec)
-        print("Servers started, creating environments...")
-
-        env = SubprocVecEnv([init_env(i, monitor=True) for i in range(n_envs)], start_method="spawn")
-        # Use single-process eval env to avoid extra subprocess fragility during callback evaluation.
-        eval_env = DummyVecEnv([init_env(n_envs, monitor=True)])
-
-        try:
-            # Custom policy network architecture
-            policy_kwargs = dict(
-                net_arch=dict(
-                    pi=[512, 256, 128],  # Policy network: 3 layers
-                    vf=[512, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
-            )
-
-            if "model_file" in args:  # retrain
-                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
-                                 batch_size=minibatch_size, learning_rate=learning_rate)
-            else:  # train new model
-                model = PPO(
-                    "MlpPolicy",
-                    env=env,
-                    verbose=1,
-                    n_steps=n_steps_per_env,
-                    batch_size=minibatch_size,
-                    learning_rate=learning_rate,
-                    device="cpu",
-                    policy_kwargs=policy_kwargs,
-                    ent_coef=float(os.environ.get("GYM_CPU_TRAIN_ENT_COEF", "0.05")),  # Entropy coefficient for exploration
-                    clip_range=float(os.environ.get("GYM_CPU_TRAIN_CLIP_RANGE", "0.2")),  # PPO clipping parameter
-                    gae_lambda=0.95,  # GAE lambda
-                    gamma=float(os.environ.get("GYM_CPU_TRAIN_GAMMA", "0.95")), # Discount factor
-                    # target_kl=0.03,
-                    n_epochs=int(os.environ.get("GYM_CPU_TRAIN_EPOCHS", "5")),
-                    tensorboard_log=f"./scripts/gyms/logs/{folder_name}/tensorboard/"
-                )
-
-            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
-                                          eval_freq=n_steps_per_env * 20, save_freq=n_steps_per_env * 20, eval_eps=7,
-                                          backup_env_file=__file__)
-        except KeyboardInterrupt:
-            sleep(1)  # wait for child processes
-            print("\nctrl+c pressed, aborting...\n")
-            servers.kill()
-            return
-
-        env.close()
-        eval_env.close()
-        servers.kill()
-
-    def test(self, args):
-
-        # Uses different server and monitor ports
-        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        test_no_render = os.environ.get("GYM_CPU_TEST_NO_RENDER", "0") == "1"
-        test_no_realtime = os.environ.get("GYM_CPU_TEST_NO_REALTIME", "0") == "1"
-
-        server = Train_Server(
-            self.server_p - 1,
-            self.monitor_p,
-            1,
-            no_render=test_no_render,
-            no_realtime=test_no_realtime,
-        )
-        env = WalkEnv(self.ip, self.server_p - 1)
-        model = PPO.load(args["model_file"], env=env)
-
-        try:
-            self.export_model(args["model_file"], args["model_file"] + ".pkl",
-                              False)  # Export to pkl to create custom behavior
-            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
-        except KeyboardInterrupt:
-            print()
-
-        env.close()
-        server.kill()
-
-
-if __name__ == "__main__":
-    from types import SimpleNamespace
-
-    # 创建默认参数
-    script_args = SimpleNamespace(
-        args=SimpleNamespace(
-            i='127.0.0.1',  # Server IP
-            p=3100,  # Server port
-            m=3200,  # Monitor port
-            r=0,  # Robot type
-            t='Gym',  # Team name
-            u=1  # Uniform number
-        )
-    )
-
-    trainer = Train(script_args)
-
-    run_mode = os.environ.get("GYM_CPU_MODE", "train").strip().lower()
-
-    if run_mode == "test":
-        test_model_file = os.environ.get("GYM_CPU_TEST_MODEL", "scripts/gyms/logs/Turn_R0_004/best_model.zip")
-        test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Turn_R0_004/")
-        trainer.test({"model_file": test_model_file, "folder_dir": test_folder})
-    else:
-        retrain_model = os.environ.get("GYM_CPU_TRAIN_MODEL", "").strip()
-        if retrain_model:
-            trainer.train({"model_file": retrain_model})
-        else:
-            trainer.train({})

scripts/gyms/logs/Turn_R0_012/Walk.py

@@ -1,853 +0,0 @@
-import os
-import numpy as np
-import math
-import time
-from time import sleep
-from random import random
-from random import uniform
-from itertools import count
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.monitor import Monitor
-from stable_baselines3.common.vec_env import SubprocVecEnv, DummyVecEnv
-
-import gymnasium as gym
-from gymnasium import spaces
-
-from scripts.commons.Train_Base import Train_Base
-from scripts.commons.Server import Server as Train_Server
-
-from agent.base_agent import Base_Agent
-from utils.math_ops import MathOps
-
-from scipy.spatial.transform import Rotation as R
-
-'''
-Objective:
-Learn how to run forward using step primitive
-----------
-- class Basic_Run: implements an OpenAI custom gym
-- class Train:  implements algorithms to train a new model or test an existing model
-'''
-
-
-class WalkEnv(gym.Env):
-    def __init__(self, ip, server_p) -> None:
-
-        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
-        self.Player = player = Base_Agent(
-            team_name="Gym",
-            number=1,
-            host=ip,
-            port=server_p
-        )
-        self.robot_type = self.Player.robot
-        self.step_counter = 0  # to limit episode size
-        self.force_play_on = True
-
-        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
-        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
-        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
-        self.isfallen = False
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.debug_every_n_steps = 5
-        self.enable_debug_joint_status = False
-        self.reward_debug_interval_sec = float(os.environ.get("GYM_CPU_REWARD_DEBUG_INTERVAL_SEC", "600"))
-        self.reward_debug_burst_steps = int(os.environ.get("GYM_CPU_REWARD_DEBUG_BURST_STEPS", "10"))
-        self._reward_debug_last_time = time.time()
-        self._reward_debug_steps_left = 0
-        self.calibrate_nominal_from_neutral = True
-        self.auto_calibrate_train_sim_flip = True
-        self.nominal_calibrated_once = False
-        self.flip_calibrated_once = False
-        self._target_hz = 0.0
-        self._target_dt = 0.0
-        self._last_sync_time = None
-        target_hz_env = 0
-        if target_hz_env:
-            try:
-                self._target_hz = float(target_hz_env)
-            except ValueError:
-                self._target_hz = 0.0
-        if self._target_hz > 0.0:
-            self._target_dt = 1.0 / self._target_hz
-
-        # State space
-        # 原始观测大小: 78
-        obs_size = 78
-        self.obs = np.zeros(obs_size, np.float32)
-        self.observation_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(obs_size,),
-            dtype=np.float32
-        )
-
-        action_dim = len(self.Player.robot.ROBOT_MOTORS)
-        self.no_of_actions = action_dim
-        self.action_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(action_dim,),
-            dtype=np.float32
-        )
-
-        # 中立姿态
-        self.joint_nominal_position = np.array(
-            [
-                0.0,  # 0: Head_yaw (he1)
-                0.0,  # 1: Head_pitch (he2)
-                0.0,  # 2: Left_Shoulder_Pitch (lae1)
-                0.0,  # 3: Left_Shoulder_Roll (lae2)
-                0.0,  # 4: Left_Elbow_Pitch (lae3)
-                0.0,  # 5: Left_Elbow_Yaw (lae4)
-                0.0,  # 6: Right_Shoulder_Pitch (rae1)
-                0.0,  # 7: Right_Shoulder_Roll (rae2)
-                0.0,  # 8: Right_Elbow_Pitch (rae3)
-                0.0,  # 9: Right_Elbow_Yaw (rae4)
-                0.0,  # 10: Waist (te1)
-                0.0,  # 11: Left_Hip_Pitch (lle1)
-                0.0,  # 12: Left_Hip_Roll (lle2)
-                1.0,  # 13: Left_Hip_Yaw (lle3)
-                0.0,  # 14: Left_Knee_Pitch (lle4)
-                0.0,  # 15: Left_Ankle_Pitch (lle5)
-                0.0,  # 16: Left_Ankle_Roll (lle6)
-                0.0,  # 17: Right_Hip_Pitch (rle1)
-                0.0,  # 18: Right_Hip_Roll (rle2)
-                1.0,  # 19: Right_Hip_Yaw (rle3)
-                0.0,  # 20: Right_Knee_Pitch (rle4)
-                0.0,  # 21: Right_Ankle_Pitch (rle5)
-                0.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-        self.joint_nominal_position = np.zeros(self.no_of_actions)
-        self.train_sim_flip = np.array(
-            [
-                1.0,  # 0: Head_yaw (he1)
-                -1.0,  # 1: Head_pitch (he2)
-                1.0,  # 2: Left_Shoulder_Pitch (lae1)
-                -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                -1.0,  # 4: Left_Elbow_Pitch (lae3)
-                1.0,  # 5: Left_Elbow_Yaw (lae4)
-                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                -1.0,  # 7: Right_Shoulder_Roll (rae2)
-                1.0,  # 8: Right_Elbow_Pitch (rae3)
-                1.0,  # 9: Right_Elbow_Yaw (rae4)
-                1.0,  # 10: Waist (te1)
-                1.0,  # 11: Left_Hip_Pitch (lle1)
-                -1.0,  # 12: Left_Hip_Roll (lle2)
-                -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,  # 14: Left_Knee_Pitch (lle4)
-                1.0,  # 15: Left_Ankle_Pitch (lle5)
-                -1.0,  # 16: Left_Ankle_Roll (lle6)
-                -1.0,  # 17: Right_Hip_Pitch (rle1)
-                -1.0,  # 18: Right_Hip_Roll (rle2)
-                -1.0,  # 19: Right_Hip_Yaw (rle3)
-                -1.0,  # 20: Right_Knee_Pitch (rle4)
-                -1.0,  # 21: Right_Ankle_Pitch (rle5)
-                -1.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-
-        self.scaling_factor = 0.3
-        # self.scaling_factor = 1
-
-        # Encourage a minimum lateral stance so the policy avoids feet overlap.
-        self.min_stance_rad = 0.10
-
-        # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = False
-        self.reset_beam_yaw_range_deg = float(os.environ.get("GYM_CPU_RESET_BEAM_YAW_RANGE_DEG", "180"))
-        self.reset_target_bearing_range_deg = float(os.environ.get("GYM_CPU_RESET_TARGET_BEARING_RANGE_DEG", "90"))
-        self.reset_target_distance_min = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MIN", "1.2"))
-        self.reset_target_distance_max = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MAX", "2.8"))
-        if self.reset_target_distance_min > self.reset_target_distance_max:
-            self.reset_target_distance_min, self.reset_target_distance_max = (
-                self.reset_target_distance_max,
-                self.reset_target_distance_min,
-            )
-        self.reset_joint_noise_rad = 0.025
-        self.reset_perturb_steps = 4
-        self.reset_recover_steps = 8
-
-        self.reward_smoothness_scale = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_SCALE", "0.06"))
-        self.reward_smoothness_cap = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_CAP", "0.45"))
-        self.reward_head_toward_bonus = float(os.environ.get("GYM_CPU_REWARD_HEAD_TOWARD_BONUS", "1"))
-
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Track previous position
-        self.last_yaw_error = None
-        self.Player.server.connect()
-        # sleep(2.0)  # Longer wait for connection to establish completely
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-        self.start_time = time.time()
-
-    def _reconnect_server(self):
-        try:
-            self.Player.server.shutdown()
-        except Exception:
-            pass
-
-        self.Player.server.connect()
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-
-    def _safe_receive_world_update(self, retries=1):
-        last_exc = None
-        for attempt in range(retries + 1):
-            try:
-                self.Player.server.receive()
-                self.Player.world.update()
-                return
-            except (ConnectionResetError, OSError) as exc:
-                last_exc = exc
-                if attempt >= retries:
-                    raise
-                self._reconnect_server()
-        if last_exc is not None:
-            raise last_exc
-
-    def debug_log(self, message):
-        print(message)
-        try:
-            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
-            with open(log_path, "a", encoding="utf-8") as f:
-                f.write(message + "\n")
-        except OSError:
-            pass
-
-    @staticmethod
-    def _wrap_to_pi(angle_rad: float) -> float:
-        return (angle_rad + math.pi) % (2.0 * math.pi) - math.pi
-
-    def observe(self, init=False):
-
-        """获取当前观测值"""
-        robot = self.Player.robot
-        world = self.Player.world
-
-        # Safety check: ensure data is available
-
-        # 计算目标速度
-        raw_target = self.target_position - world.global_position[:2]
-        velocity = MathOps.rotate_2d_vec(
-            raw_target,
-            -robot.global_orientation_euler[2],
-            is_rad=False
-        )
-
-        # 计算相对方向
-        rel_orientation = MathOps.vector_angle(velocity) * 0.3
-        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
-
-        velocity = np.concatenate([velocity, np.array([rel_orientation])])
-        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
-        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
-
-        # 关节状态
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        qpos_qvel_previous_action = np.concatenate([
-            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
-            radian_joint_speeds / 110.0 * self.train_sim_flip,
-            self.previous_action / 10.0,
-        ])
-
-        # 角速度
-        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
-
-        # 投影的重力方向
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-
-        # 组合观测
-        observation = np.concatenate([
-            qpos_qvel_previous_action,
-            ang_vel,
-            velocity,
-            projected_gravity,
-        ])
-
-        observation = np.clip(observation, -10.0, 10.0)
-        return observation.astype(np.float32)
-
-    def sync(self):
-        ''' Run a single simulation step '''
-        self._safe_receive_world_update(retries=1)
-        self.Player.robot.commit_motor_targets_pd()
-        self.Player.server.send()
-        if self._target_dt > 0.0:
-            now = time.time()
-            if self._last_sync_time is None:
-                self._last_sync_time = now
-                return
-            elapsed = now - self._last_sync_time
-            remaining = self._target_dt - elapsed
-            if remaining > 0.0:
-                time.sleep(remaining)
-                now = time.time()
-            self._last_sync_time = now
-
-    def debug_joint_status(self):
-        robot = self.Player.robot
-        actual_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        target_joint_positions = getattr(
-            self,
-            'target_joint_positions',
-            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
-        )
-        joint_error = actual_joint_positions - target_joint_positions
-        leg_slice = slice(11, None)
-
-        self.debug_log(
-            "[WalkDebug] "
-            f"step={self.step_counter} "
-            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
-            f"target_xy={np.round(self.target_position, 3).tolist()} "
-            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
-            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
-            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
-            f"fallen={self.Player.world.global_position[2] < 0.3}"
-        )
-        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
-
-    def reset(self, seed=None, options=None):
-        '''
-        Reset and stabilize the robot
-        Note: for some behaviors it would be better to reduce stabilization or add noise
-        '''
-        r = self.Player.robot
-        super().reset(seed=seed)
-        if seed is not None:
-            np.random.seed(seed)
-
-        target_distance = np.random.uniform(self.reset_target_distance_min, self.reset_target_distance_max)
-        target_bearing_deg = np.random.uniform(-self.reset_target_bearing_range_deg, self.reset_target_bearing_range_deg)
-
-        self.step_counter = 0
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
-        self.last_yaw_error = None
-        self.walk_cycle_step = 0
-        self._reward_debug_steps_left = 0
-
-        # 随机 beam 目标位置和朝向，增加训练多样性
-        beam_x = (random() - 0.5) * 10
-        beam_y = (random() - 0.5) * 10
-        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
-
-        for _ in range(5):
-            self._safe_receive_world_update(retries=2)
-            self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
-            self.Player.server.send()
-
-        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
-        finished_count = 0
-        for _ in range(50):
-            finished = self.Player.skills_manager.execute("Neutral")
-            self.sync()
-            if finished:
-                finished_count += 1
-                if finished_count >= 20:  # 假设需要连续20次完成才算成功
-                    break
-
-        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-            # Perturb waist + lower body only (10:), keep head/arms stable.
-            perturb_action[10:] = np.random.uniform(
-                -self.reset_joint_noise_rad,
-                self.reset_joint_noise_rad,
-                size=(self.no_of_actions - 10,)
-            )
-
-            for _ in range(self.reset_perturb_steps):
-                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-            for i in range(self.reset_recover_steps):
-                # Linearly fade perturbation to help policy start from near-neutral.
-                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-        # memory variables
-        self.sync()  
-        self.initial_position = np.array(self.Player.world.global_position[:2])
-        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions, np.float32)
-        # Randomize global target bearing so policy must learn to rotate toward it first.
-        heading_deg = float(r.global_orientation_euler[2])
-        target_offset = MathOps.rotate_2d_vec(
-            np.array([target_distance, 0.0]),
-            heading_deg + target_bearing_deg,
-            is_rad=False,
-        )
-        point1 = self.initial_position + target_offset
-        self.point_list = [point1]
-        self.target_position = self.point_list[self.waypoint_index]
-        self.initial_height = self.Player.world.global_position[2]
-
-        return self.observe(True), {}
-
-    def render(self, mode='human', close=False):
-        return
-
-    
-    def compute_reward(self, previous_pos, current_pos, action):
-        height = float(self.Player.world.global_position[2])
-        robot = self.Player.robot
-
-
-        joint_pos_rad = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        joint_speed_rad = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-        tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        ang_vel = np.deg2rad(robot.gyroscope)
-        rp_ang_vel_mag = float(np.linalg.norm(ang_vel[:2]))
-
-        is_fallen = height < 0.55
-        if is_fallen:
-            # remain = max(0, 800 - self.step_counter)
-            # return -8.0 - 0.01 * remain
-            return -20.0
-        
-
-        if np.linalg.norm(current_pos - previous_pos) > 0.005:
-            position_penalty = -3 * float(np.linalg.norm(current_pos - previous_pos))
-        else:
-            position_penalty = 0.0
-
-
-        # Turn-to-target shaping.
-        to_target = self.target_position - current_pos
-        dist_to_target = float(np.linalg.norm(to_target))
-        if dist_to_target > 1e-6:
-            target_yaw = math.atan2(float(to_target[1]), float(to_target[0]))
-        else:
-            target_yaw = 0.0
-
-        robot_yaw = math.radians(float(robot.global_orientation_euler[2]))
-        yaw_error = target_yaw - robot_yaw
-
-        # Main heading objective: face the target direction.
-        # heading_align_reward = 1.0 * math.cos(yaw_error)
-
-        abs_yaw_error = abs(yaw_error)
-        alive_bonus = 2.0 * max(0.0, 1.0 - abs_yaw_error / math.pi)
-        head_toward_bonus = self.reward_head_toward_bonus if abs_yaw_error < math.radians(4.0) else 0.0
-
-        if self.last_yaw_error is None:
-            heading_progress_reward = 0.0
-        else:
-            prev_abs_yaw_error = abs(self.last_yaw_error)
-            yaw_err_delta = prev_abs_yaw_error - abs_yaw_error
-            progress_gate = 1.0 if abs_yaw_error > math.radians(4.0) else 0.0
-            heading_progress_reward =  0.8 * progress_gate * yaw_err_delta
-            heading_progress_reward = float(np.clip(heading_progress_reward, -0.4, 0.4))
-        self.last_yaw_error = yaw_error
-
-        # action_penalty = -0.01 * float(np.linalg.norm(action))
-        smoothness_penalty = -0.05 * float(np.linalg.norm(action - self.last_action_for_reward))
-
-        posture_penalty = -0.6 * tilt_mag
-        # Penalize roll/pitch rotational shake but do not penalize yaw turning directly.
-        ang_vel_penalty = -0.06 * rp_ang_vel_mag
-
-        joint_pos = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        ) * self.train_sim_flip
-        left_hip_roll = float(joint_pos[12])
-        right_hip_roll = float(joint_pos[18])
-        left_hip_pitch = float(joint_pos[11])
-        right_hip_pitch = float(joint_pos[17])
-
-        left_ankle_roll = float(joint_pos[16])
-        right_ankle_roll = float(joint_pos[22])
-
-        max_leg_roll = 0.2  # 防止劈叉姿势
-        split_penalty = -0.8 * max(0.0, (-left_hip_roll + right_hip_roll - 2 * max_leg_roll) / max_leg_roll)
-        left_hip_yaw = float(joint_pos[13])
-        right_hip_yaw = float(joint_pos[19])
-
-        min_leg_separation = 0.05  # 最小腿间距（防止贴得太近）
-        # 惩罚腿过分靠拢（内收）- 基于两腿间距
-        leg_separation = -left_hip_roll + right_hip_roll
-        inward_penalty = -0.25 * max(0.0, (min_leg_separation - leg_separation) / min_leg_separation)
-        
-        
-        # 脚踝roll角度检测：防止过度外翻或内翻
-        max_ankle_roll = 0.15  # 最大允许的脚踝roll角度
-        
-        # 惩罚脚踝过度外翻/内翻（绝对值过大）
-        ankle_roll_penalty = -0.5 * max(0.0, (abs(left_ankle_roll) + abs(right_ankle_roll) - 2 * max_ankle_roll) / max_ankle_roll)
-        
-        # 惩罚两脚踝roll方向相反（不稳定姿势）
-        ankle_roll_cross_penalty = -0.3 * max(0.0, -(left_ankle_roll * right_ankle_roll))
-
-       # 分别惩罚左右大腿过度转动
-        max_hip_yaw = 0.4  # 最大允许的yaw角度
-        left_hip_yaw_penalty = -0.4 * max(0.0, abs(left_hip_yaw) - max_hip_yaw)
-        right_hip_yaw_penalty = -0.4 * max(0.0, abs(right_hip_yaw) - max_hip_yaw)
-        # 智能交叉腿惩罚：只在站立时惩罚，转身时允许交叉腿
-        yaw_rate = float(np.deg2rad(robot.gyroscope[2]))
-        yaw_rate_abs = abs(yaw_rate)
-
-        # 当转身速度较小时才惩罚交叉腿（站立状态）
-        cross_leg_gate = max(0.0, 1.0 - yaw_rate_abs / math.radians(8.0))
-        hip_yaw_cross_penalty = -1.0 * cross_leg_gate * max(0.0, -(left_hip_yaw * right_hip_yaw)) if left_hip_yaw > 0 and right_hip_yaw < 0 else 0.0
-
-        # Torso-lower-body linkage: reward coordinated turning, punish waist-only spinning.
-        waist_speed = abs(float(joint_speed_rad[10]))
-        lower_body_speed = float(np.mean(np.abs(joint_speed_rad[11:23])))
-        lower_body_follow_ratio = lower_body_speed / (waist_speed + 1e-4)
-        linkage_reward = 0.24 * min(1.0, lower_body_follow_ratio) * min(1.0, waist_speed / 1.2)
-        waist_only_turn_penalty = -0.20 * max(0.0, waist_speed - 1.35 * lower_body_speed)
-
-        # Extra posture linkage in yaw joints to avoid decoupled torso twist.
-        waist_yaw = abs(float(joint_pos_rad[10]))
-        hip_yaw_mean = 0.5 * (abs(float(joint_pos_rad[13])) + abs(float(joint_pos_rad[19])))
-        yaw_link_reward = 0.12 * math.exp(-abs(waist_yaw - hip_yaw_mean) / 0.22)
-
-        target_height = self.initial_height
-        height_error =  height - target_height
-        height_error = height - target_height
-        
-        height_penalty = -(math.exp(12*abs(height_error))-1) if height_error > 0.04 else 0
-
-        # # 在 compute_reward 开头附近，添加高度变化率计算
-        # if not hasattr(self, 'last_height'):
-        #     self.last_height = height
-        #     self.last_height_time = self.step_counter  # 可选，用于时间间隔
-        # height_rate = height - self.last_height  # 正为上升，负为下降
-        # self.last_height = height
-
-        # 惩罚高度下降（负变化率）
-        # height_down_penalty = -5.0 * max(0, -height_rate)  # 系数可调，-height_rate 为正表示下降幅度
-
-        # # 在 compute_reward 中
-        # if self.step_counter > 50:
-        #     avg_prev_action = np.mean(self.prev_action_history, axis=0)
-        #     novelty = float(np.linalg.norm(action - avg_prev_action))
-        #     exploration_bonus = 0.05 * novelty
-        # else:
-        #     exploration_bonus = 0
-
-        # self.prev_action_history[self.history_idx] = action
-        # self.history_idx = (self.history_idx + 1) % 50
-
-
-        total = (
-            # progress_reward  + 
-                alive_bonus + 
-                head_toward_bonus +
-                heading_progress_reward +
-                # lateral_penalty +
-                # action_penalty +
-                smoothness_penalty +
-                posture_penalty
-                + ang_vel_penalty
-                + height_penalty
-                + ankle_roll_penalty
-                + ankle_roll_cross_penalty
-                + split_penalty
-                + inward_penalty
-                # + leg_proximity_penalty
-                # + left_hip_yaw_penalty
-                # + right_hip_yaw_penalty
-                # + hip_yaw_cross_penalty
-                + position_penalty
-                # + linkage_reward
-                # + waist_only_turn_penalty
-                # + yaw_link_reward
-                # + stance_collapse_penalty
-                # + hip_yaw_yaw_cross_penalty
-                # + stance_collapse_penalty
-                #  + cross_leg_penalty
-                #  + exploration_bonus
-                # + height_down_penalty
-                 )
-        # print(height_error, height_penalty)
-        
-        now = time.time()
-        if self.reward_debug_interval_sec > 0 and now - self._reward_debug_last_time >= self.reward_debug_interval_sec:
-            self._reward_debug_last_time = now
-            self._reward_debug_steps_left = max(1, self.reward_debug_burst_steps)
-
-        if self._reward_debug_steps_left > 0:
-            self._reward_debug_steps_left -= 1
-            self.debug_log(
-                f"height_penalty:{height_penalty:.4f},"
-                f"smoothness_penalty:{smoothness_penalty:.4f},"
-                f"posture_penalty:{posture_penalty:.4f},"
-                f"heading_progress_reward:{heading_progress_reward:.4f},"
-                # f"stance_collapse_penalty:{stance_collapse_penalty:.4f},"
-                # f"cross_leg_penalty:{cross_leg_penalty:.4f},"
-                f"ang_vel_penalty:{ang_vel_penalty:.4f},"
-                f"split_penalty:{split_penalty:.4f},"
-                f"ankle_roll_penalty:{ankle_roll_penalty:.4f},"
-                f"ankle_roll_cross_penalty:{ankle_roll_cross_penalty:.4f},"
-                f"left_hip_yaw_penalty:{left_hip_yaw_penalty:.4f},"
-                f"right_hip_yaw_penalty:{right_hip_yaw_penalty:.4f},"
-                f"hip_yaw_cross_penalty:{hip_yaw_cross_penalty:.4f},"
-                f"inward_penalty:{inward_penalty:.4f},"
-                f"position_penalty:{position_penalty:.4f},"
-                # f"linkage_reward:{linkage_reward:.4f},"
-                # f"waist_only_turn_penalty:{waist_only_turn_penalty:.4f},"
-                # f"yaw_link_reward:{yaw_link_reward:.4f}"
-                # f"leg_proximity_penalty:{leg_proximity_penalty:.4f},"
-
-                # f"stance_collapse_penalty:{stance_collapse_penalty:.4f},"
-                # f"hip_yaw_yaw_cross_penalty:{hip_yaw_yaw_cross_penalty:.4f},"
-                #   f"height_down_penalty:{height_down_penalty:.4f}",
-                #   f"exploration_bonus:{exploration_bonus:.4f}"
-                f"alive_bonus:{alive_bonus:.4f},"
-                f"abs_yaw_error:{abs_yaw_error:.4f}"
-                f"total:{total:.4f}"
-                )
-        # print(f"abs_yaw_error:{abs_yaw_error:.4f}")
-        return total
-
-
-
-    def step(self, action):
-
-        r = self.Player.robot
-        max_action_delta =  0.5# Limit how much the action can change from the previous step to encourage smoother motions.
-        if self.previous_action is not None:
-            action = np.clip(action, self.previous_action - max_action_delta, self.previous_action + max_action_delta)
-        action[0:2] = 0
-        action[3] = 4
-        action[7] = -4
-        action[2] = 0
-        action[6] = 0
-        action[4] = 0
-        action[5] = -5
-        action[8] = 0
-        action[9] = 5
-        action[10] = 0
-        action[11] = np.clip(action[11], -0.4, 0.4)
-        action[17] = np.clip(action[17], -0.4, 0.4)
-        # action[12] = -1.0
-        # action[18] = 1.0
-        # action[13] = -1.0
-        # action[19] = 1.0
-        self.previous_action = action.copy()
-
-        self.target_joint_positions = (
-                # self.joint_nominal_position +
-                 self.scaling_factor * action
-        )
-        self.target_joint_positions *= self.train_sim_flip
-
-        for idx, target in enumerate(self.target_joint_positions):
-            r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=110, kd=6
-            )
-
-        self.previous_action = action.copy()
-
-        self.sync()  # run simulation step
-        self.step_counter += 1
-
-        if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
-            self.debug_joint_status()
-
-        current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
-
-        if self.step_counter % 10 == 0:
-            self.previous_pos = current_pos.copy()
-
-        # Compute reward based on movement from previous step
-        reward = self.compute_reward(self.previous_pos, current_pos, action)
-
-        self.last_action_for_reward = action.copy()
-
-        # Fall detection and penalty
-        is_fallen = self.Player.world.global_position[2] < 0.55
-
-        # terminal state: the robot is falling or timeout
-        terminated = is_fallen or self.step_counter > 800 or self.route_completed
-        truncated = False
-
-        return self.observe(), reward, terminated, truncated, {}
-
-
-class Train(Train_Base):
-    def __init__(self, script) -> None:
-        super().__init__(script)
-
-    def train(self, args):
-
-        # --------------------------------------- Learning parameters
-        n_envs = int(os.environ.get("GYM_CPU_N_ENVS", "20"))
-        if n_envs < 1:
-            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
-        server_warmup_sec = float(os.environ.get("GYM_CPU_SERVER_WARMUP_SEC", "3.0"))
-        n_steps_per_env = int(os.environ.get("GYM_CPU_TRAIN_STEPS_PER_ENV", "512"))  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = int(os.environ.get("GYM_CPU_TRAIN_BATCH_SIZE", "512"))  # should be a factor of (n_steps_per_env * n_envs)
-        total_steps = 30000000
-        learning_rate = float(os.environ.get("GYM_CPU_TRAIN_LR", "3e-4"))
-        folder_name = f'Turn_R{self.robot_type}'
-        model_path = f'./scripts/gyms/logs/{folder_name}/'
-
-        print(f"Model path: {model_path}")
-        print(f"Using {n_envs} parallel environments")
-
-        # --------------------------------------- Run algorithm
-        def init_env(i_env, monitor=False):
-            def thunk():
-                env = WalkEnv(self.ip, self.server_p + i_env)
-                if monitor:
-                    env = Monitor(env)
-                return env
-
-            return thunk
-
-        server_log_dir = os.path.join(model_path, "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1, no_render=True, no_realtime=True)  # include 1 extra server for testing
-
-        # Wait for servers to start
-        print(f"Starting {n_envs + 1} rcssservermj servers...")
-        if server_warmup_sec > 0:
-            print(f"Waiting {server_warmup_sec:.1f}s for server warmup...")
-            sleep(server_warmup_sec)
-        print("Servers started, creating environments...")
-
-        env = SubprocVecEnv([init_env(i, monitor=True) for i in range(n_envs)], start_method="spawn")
-        # Use single-process eval env to avoid extra subprocess fragility during callback evaluation.
-        eval_env = DummyVecEnv([init_env(n_envs, monitor=True)])
-
-        try:
-            # Custom policy network architecture
-            policy_kwargs = dict(
-                net_arch=dict(
-                    pi=[512, 256, 128],  # Policy network: 3 layers
-                    vf=[512, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
-            )
-
-            if "model_file" in args:  # retrain
-                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
-                                 batch_size=minibatch_size, learning_rate=learning_rate)
-            else:  # train new model
-                model = PPO(
-                    "MlpPolicy",
-                    env=env,
-                    verbose=1,
-                    n_steps=n_steps_per_env,
-                    batch_size=minibatch_size,
-                    learning_rate=learning_rate,
-                    device="cpu",
-                    policy_kwargs=policy_kwargs,
-                    ent_coef=float(os.environ.get("GYM_CPU_TRAIN_ENT_COEF", "0.05")),  # Entropy coefficient for exploration
-                    clip_range=float(os.environ.get("GYM_CPU_TRAIN_CLIP_RANGE", "0.2")),  # PPO clipping parameter
-                    gae_lambda=0.95,  # GAE lambda
-                    gamma=float(os.environ.get("GYM_CPU_TRAIN_GAMMA", "0.95")), # Discount factor
-                    # target_kl=0.03,
-                    n_epochs=int(os.environ.get("GYM_CPU_TRAIN_EPOCHS", "5")),
-                    tensorboard_log=f"./scripts/gyms/logs/{folder_name}/tensorboard/"
-                )
-
-            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
-                                          eval_freq=n_steps_per_env * 20, save_freq=n_steps_per_env * 20, eval_eps=7,
-                                          backup_env_file=__file__)
-        except KeyboardInterrupt:
-            sleep(1)  # wait for child processes
-            print("\nctrl+c pressed, aborting...\n")
-            servers.kill()
-            return
-
-        env.close()
-        eval_env.close()
-        servers.kill()
-
-    def test(self, args):
-
-        # Uses different server and monitor ports
-        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        test_no_render = os.environ.get("GYM_CPU_TEST_NO_RENDER", "0") == "1"
-        test_no_realtime = os.environ.get("GYM_CPU_TEST_NO_REALTIME", "0") == "1"
-
-        server = Train_Server(
-            self.server_p - 1,
-            self.monitor_p,
-            1,
-            no_render=test_no_render,
-            no_realtime=test_no_realtime,
-        )
-        env = WalkEnv(self.ip, self.server_p - 1)
-        model = PPO.load(args["model_file"], env=env)
-
-        try:
-            self.export_model(args["model_file"], args["model_file"] + ".pkl",
-                              False)  # Export to pkl to create custom behavior
-            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
-        except KeyboardInterrupt:
-            print()
-
-        env.close()
-        server.kill()
-
-
-if __name__ == "__main__":
-    from types import SimpleNamespace
-
-    # 创建默认参数
-    script_args = SimpleNamespace(
-        args=SimpleNamespace(
-            i='127.0.0.1',  # Server IP
-            p=3100,  # Server port
-            m=3200,  # Monitor port
-            r=0,  # Robot type
-            t='Gym',  # Team name
-            u=1  # Uniform number
-        )
-    )
-
-    trainer = Train(script_args)
-
-    run_mode = os.environ.get("GYM_CPU_MODE", "train").strip().lower()
-
-    if run_mode == "test":
-        test_model_file = os.environ.get("GYM_CPU_TEST_MODEL", "scripts/gyms/logs/Turn_R0_004/best_model.zip")
-        test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Turn_R0_004/")
-        trainer.test({"model_file": test_model_file, "folder_dir": test_folder})
-    else:
-        retrain_model = os.environ.get("GYM_CPU_TRAIN_MODEL", "").strip()
-        if retrain_model:
-            trainer.train({"model_file": retrain_model})
-        else:
-            trainer.train({})

scripts/gyms/logs/Turn_R0_013/Walk.py

@@ -1,853 +0,0 @@
-import os
-import numpy as np
-import math
-import time
-from time import sleep
-from random import random
-from random import uniform
-from itertools import count
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.monitor import Monitor
-from stable_baselines3.common.vec_env import SubprocVecEnv, DummyVecEnv
-
-import gymnasium as gym
-from gymnasium import spaces
-
-from scripts.commons.Train_Base import Train_Base
-from scripts.commons.Server import Server as Train_Server
-
-from agent.base_agent import Base_Agent
-from utils.math_ops import MathOps
-
-from scipy.spatial.transform import Rotation as R
-
-'''
-Objective:
-Learn how to run forward using step primitive
-----------
-- class Basic_Run: implements an OpenAI custom gym
-- class Train:  implements algorithms to train a new model or test an existing model
-'''
-
-
-class WalkEnv(gym.Env):
-    def __init__(self, ip, server_p) -> None:
-
-        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
-        self.Player = player = Base_Agent(
-            team_name="Gym",
-            number=1,
-            host=ip,
-            port=server_p
-        )
-        self.robot_type = self.Player.robot
-        self.step_counter = 0  # to limit episode size
-        self.force_play_on = True
-
-        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
-        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
-        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
-        self.isfallen = False
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.debug_every_n_steps = 5
-        self.enable_debug_joint_status = False
-        self.reward_debug_interval_sec = float(os.environ.get("GYM_CPU_REWARD_DEBUG_INTERVAL_SEC", "600"))
-        self.reward_debug_burst_steps = int(os.environ.get("GYM_CPU_REWARD_DEBUG_BURST_STEPS", "10"))
-        self._reward_debug_last_time = time.time()
-        self._reward_debug_steps_left = 0
-        self.calibrate_nominal_from_neutral = True
-        self.auto_calibrate_train_sim_flip = True
-        self.nominal_calibrated_once = False
-        self.flip_calibrated_once = False
-        self._target_hz = 0.0
-        self._target_dt = 0.0
-        self._last_sync_time = None
-        target_hz_env = 0
-        if target_hz_env:
-            try:
-                self._target_hz = float(target_hz_env)
-            except ValueError:
-                self._target_hz = 0.0
-        if self._target_hz > 0.0:
-            self._target_dt = 1.0 / self._target_hz
-
-        # State space
-        # 原始观测大小: 78
-        obs_size = 78
-        self.obs = np.zeros(obs_size, np.float32)
-        self.observation_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(obs_size,),
-            dtype=np.float32
-        )
-
-        action_dim = len(self.Player.robot.ROBOT_MOTORS)
-        self.no_of_actions = action_dim
-        self.action_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(action_dim,),
-            dtype=np.float32
-        )
-
-        # 中立姿态
-        self.joint_nominal_position = np.array(
-            [
-                0.0,  # 0: Head_yaw (he1)
-                0.0,  # 1: Head_pitch (he2)
-                0.0,  # 2: Left_Shoulder_Pitch (lae1)
-                0.0,  # 3: Left_Shoulder_Roll (lae2)
-                0.0,  # 4: Left_Elbow_Pitch (lae3)
-                0.0,  # 5: Left_Elbow_Yaw (lae4)
-                0.0,  # 6: Right_Shoulder_Pitch (rae1)
-                0.0,  # 7: Right_Shoulder_Roll (rae2)
-                0.0,  # 8: Right_Elbow_Pitch (rae3)
-                0.0,  # 9: Right_Elbow_Yaw (rae4)
-                0.0,  # 10: Waist (te1)
-                0.0,  # 11: Left_Hip_Pitch (lle1)
-                0.0,  # 12: Left_Hip_Roll (lle2)
-                1.0,  # 13: Left_Hip_Yaw (lle3)
-                0.0,  # 14: Left_Knee_Pitch (lle4)
-                0.0,  # 15: Left_Ankle_Pitch (lle5)
-                0.0,  # 16: Left_Ankle_Roll (lle6)
-                0.0,  # 17: Right_Hip_Pitch (rle1)
-                0.0,  # 18: Right_Hip_Roll (rle2)
-                1.0,  # 19: Right_Hip_Yaw (rle3)
-                0.0,  # 20: Right_Knee_Pitch (rle4)
-                0.0,  # 21: Right_Ankle_Pitch (rle5)
-                0.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-        self.joint_nominal_position = np.zeros(self.no_of_actions)
-        self.train_sim_flip = np.array(
-            [
-                1.0,  # 0: Head_yaw (he1)
-                -1.0,  # 1: Head_pitch (he2)
-                1.0,  # 2: Left_Shoulder_Pitch (lae1)
-                -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                -1.0,  # 4: Left_Elbow_Pitch (lae3)
-                1.0,  # 5: Left_Elbow_Yaw (lae4)
-                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                -1.0,  # 7: Right_Shoulder_Roll (rae2)
-                1.0,  # 8: Right_Elbow_Pitch (rae3)
-                1.0,  # 9: Right_Elbow_Yaw (rae4)
-                1.0,  # 10: Waist (te1)
-                1.0,  # 11: Left_Hip_Pitch (lle1)
-                -1.0,  # 12: Left_Hip_Roll (lle2)
-                -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,  # 14: Left_Knee_Pitch (lle4)
-                1.0,  # 15: Left_Ankle_Pitch (lle5)
-                -1.0,  # 16: Left_Ankle_Roll (lle6)
-                -1.0,  # 17: Right_Hip_Pitch (rle1)
-                -1.0,  # 18: Right_Hip_Roll (rle2)
-                -1.0,  # 19: Right_Hip_Yaw (rle3)
-                -1.0,  # 20: Right_Knee_Pitch (rle4)
-                -1.0,  # 21: Right_Ankle_Pitch (rle5)
-                -1.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-
-        self.scaling_factor = 0.3
-        # self.scaling_factor = 1
-
-        # Encourage a minimum lateral stance so the policy avoids feet overlap.
-        self.min_stance_rad = 0.10
-
-        # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = False
-        self.reset_beam_yaw_range_deg = float(os.environ.get("GYM_CPU_RESET_BEAM_YAW_RANGE_DEG", "180"))
-        self.reset_target_bearing_range_deg = float(os.environ.get("GYM_CPU_RESET_TARGET_BEARING_RANGE_DEG", "90"))
-        self.reset_target_distance_min = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MIN", "1.2"))
-        self.reset_target_distance_max = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MAX", "2.8"))
-        if self.reset_target_distance_min > self.reset_target_distance_max:
-            self.reset_target_distance_min, self.reset_target_distance_max = (
-                self.reset_target_distance_max,
-                self.reset_target_distance_min,
-            )
-        self.reset_joint_noise_rad = 0.025
-        self.reset_perturb_steps = 4
-        self.reset_recover_steps = 8
-
-        self.reward_smoothness_scale = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_SCALE", "0.06"))
-        self.reward_smoothness_cap = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_CAP", "0.45"))
-        self.reward_head_toward_bonus = float(os.environ.get("GYM_CPU_REWARD_HEAD_TOWARD_BONUS", "1"))
-
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Track previous position
-        self.last_yaw_error = None
-        self.Player.server.connect()
-        # sleep(2.0)  # Longer wait for connection to establish completely
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-        self.start_time = time.time()
-
-    def _reconnect_server(self):
-        try:
-            self.Player.server.shutdown()
-        except Exception:
-            pass
-
-        self.Player.server.connect()
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-
-    def _safe_receive_world_update(self, retries=1):
-        last_exc = None
-        for attempt in range(retries + 1):
-            try:
-                self.Player.server.receive()
-                self.Player.world.update()
-                return
-            except (ConnectionResetError, OSError) as exc:
-                last_exc = exc
-                if attempt >= retries:
-                    raise
-                self._reconnect_server()
-        if last_exc is not None:
-            raise last_exc
-
-    def debug_log(self, message):
-        print(message)
-        try:
-            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
-            with open(log_path, "a", encoding="utf-8") as f:
-                f.write(message + "\n")
-        except OSError:
-            pass
-
-    @staticmethod
-    def _wrap_to_pi(angle_rad: float) -> float:
-        return (angle_rad + math.pi) % (2.0 * math.pi) - math.pi
-
-    def observe(self, init=False):
-
-        """获取当前观测值"""
-        robot = self.Player.robot
-        world = self.Player.world
-
-        # Safety check: ensure data is available
-
-        # 计算目标速度
-        raw_target = self.target_position - world.global_position[:2]
-        velocity = MathOps.rotate_2d_vec(
-            raw_target,
-            -robot.global_orientation_euler[2],
-            is_rad=False
-        )
-
-        # 计算相对方向
-        rel_orientation = MathOps.vector_angle(velocity) * 0.3
-        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
-
-        velocity = np.concatenate([velocity, np.array([rel_orientation])])
-        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
-        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
-
-        # 关节状态
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        qpos_qvel_previous_action = np.concatenate([
-            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
-            radian_joint_speeds / 110.0 * self.train_sim_flip,
-            self.previous_action / 10.0,
-        ])
-
-        # 角速度
-        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
-
-        # 投影的重力方向
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-
-        # 组合观测
-        observation = np.concatenate([
-            qpos_qvel_previous_action,
-            ang_vel,
-            velocity,
-            projected_gravity,
-        ])
-
-        observation = np.clip(observation, -10.0, 10.0)
-        return observation.astype(np.float32)
-
-    def sync(self):
-        ''' Run a single simulation step '''
-        self._safe_receive_world_update(retries=1)
-        self.Player.robot.commit_motor_targets_pd()
-        self.Player.server.send()
-        if self._target_dt > 0.0:
-            now = time.time()
-            if self._last_sync_time is None:
-                self._last_sync_time = now
-                return
-            elapsed = now - self._last_sync_time
-            remaining = self._target_dt - elapsed
-            if remaining > 0.0:
-                time.sleep(remaining)
-                now = time.time()
-            self._last_sync_time = now
-
-    def debug_joint_status(self):
-        robot = self.Player.robot
-        actual_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        target_joint_positions = getattr(
-            self,
-            'target_joint_positions',
-            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
-        )
-        joint_error = actual_joint_positions - target_joint_positions
-        leg_slice = slice(11, None)
-
-        self.debug_log(
-            "[WalkDebug] "
-            f"step={self.step_counter} "
-            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
-            f"target_xy={np.round(self.target_position, 3).tolist()} "
-            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
-            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
-            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
-            f"fallen={self.Player.world.global_position[2] < 0.3}"
-        )
-        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
-
-    def reset(self, seed=None, options=None):
-        '''
-        Reset and stabilize the robot
-        Note: for some behaviors it would be better to reduce stabilization or add noise
-        '''
-        r = self.Player.robot
-        super().reset(seed=seed)
-        if seed is not None:
-            np.random.seed(seed)
-
-        target_distance = np.random.uniform(self.reset_target_distance_min, self.reset_target_distance_max)
-        target_bearing_deg = np.random.uniform(-self.reset_target_bearing_range_deg, self.reset_target_bearing_range_deg)
-
-        self.step_counter = 0
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
-        self.last_yaw_error = None
-        self.walk_cycle_step = 0
-        self._reward_debug_steps_left = 0
-
-        # 随机 beam 目标位置和朝向，增加训练多样性
-        beam_x = (random() - 0.5) * 10
-        beam_y = (random() - 0.5) * 10
-        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
-
-        for _ in range(5):
-            self._safe_receive_world_update(retries=2)
-            self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
-            self.Player.server.send()
-
-        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
-        finished_count = 0
-        for _ in range(50):
-            finished = self.Player.skills_manager.execute("Neutral")
-            self.sync()
-            if finished:
-                finished_count += 1
-                if finished_count >= 20:  # 假设需要连续20次完成才算成功
-                    break
-
-        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-            # Perturb waist + lower body only (10:), keep head/arms stable.
-            perturb_action[10:] = np.random.uniform(
-                -self.reset_joint_noise_rad,
-                self.reset_joint_noise_rad,
-                size=(self.no_of_actions - 10,)
-            )
-
-            for _ in range(self.reset_perturb_steps):
-                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-            for i in range(self.reset_recover_steps):
-                # Linearly fade perturbation to help policy start from near-neutral.
-                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-        # memory variables
-        self.sync()  
-        self.initial_position = np.array(self.Player.world.global_position[:2])
-        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions, np.float32)
-        # Randomize global target bearing so policy must learn to rotate toward it first.
-        heading_deg = float(r.global_orientation_euler[2])
-        target_offset = MathOps.rotate_2d_vec(
-            np.array([target_distance, 0.0]),
-            heading_deg + target_bearing_deg,
-            is_rad=False,
-        )
-        point1 = self.initial_position + target_offset
-        self.point_list = [point1]
-        self.target_position = self.point_list[self.waypoint_index]
-        self.initial_height = self.Player.world.global_position[2]
-
-        return self.observe(True), {}
-
-    def render(self, mode='human', close=False):
-        return
-
-    
-    def compute_reward(self, previous_pos, current_pos, action):
-        height = float(self.Player.world.global_position[2])
-        robot = self.Player.robot
-
-
-        joint_pos_rad = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        joint_speed_rad = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-        tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        ang_vel = np.deg2rad(robot.gyroscope)
-        rp_ang_vel_mag = float(np.linalg.norm(ang_vel[:2]))
-
-        is_fallen = height < 0.55
-        if is_fallen:
-            # remain = max(0, 800 - self.step_counter)
-            # return -8.0 - 0.01 * remain
-            return -20.0
-        
-
-        if np.linalg.norm(current_pos - previous_pos) > 0.005:
-            position_penalty = -3 * float(np.linalg.norm(current_pos - previous_pos))
-        else:
-            position_penalty = 0.0
-
-
-        # Turn-to-target shaping.
-        to_target = self.target_position - current_pos
-        dist_to_target = float(np.linalg.norm(to_target))
-        if dist_to_target > 1e-6:
-            target_yaw = math.atan2(float(to_target[1]), float(to_target[0]))
-        else:
-            target_yaw = 0.0
-
-        robot_yaw = math.radians(float(robot.global_orientation_euler[2]))
-        yaw_error = target_yaw - robot_yaw
-
-        # Main heading objective: face the target direction.
-        # heading_align_reward = 1.0 * math.cos(yaw_error)
-
-        abs_yaw_error = abs(yaw_error)
-        alive_bonus = 2.0 * max(0.0, 1.0 - abs_yaw_error / math.pi)
-        head_toward_bonus = self.reward_head_toward_bonus if abs_yaw_error < math.radians(4.0) else 0.0
-
-        if self.last_yaw_error is None:
-            heading_progress_reward = 0.0
-        else:
-            prev_abs_yaw_error = abs(self.last_yaw_error)
-            yaw_err_delta = prev_abs_yaw_error - abs_yaw_error
-            progress_gate = 1.0 if abs_yaw_error > math.radians(4.0) else 0.0
-            heading_progress_reward =  0.8 * progress_gate * yaw_err_delta
-            heading_progress_reward = float(np.clip(heading_progress_reward, -0.4, 0.4))
-        self.last_yaw_error = yaw_error
-
-        # action_penalty = -0.01 * float(np.linalg.norm(action))
-        smoothness_penalty = -0.05 * float(np.linalg.norm(action - self.last_action_for_reward))
-
-        posture_penalty = -0.6 * tilt_mag
-        # Penalize roll/pitch rotational shake but do not penalize yaw turning directly.
-        ang_vel_penalty = -0.06 * rp_ang_vel_mag
-
-        joint_pos = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        ) * self.train_sim_flip
-        left_hip_roll = float(joint_pos[12])
-        right_hip_roll = float(joint_pos[18])
-        left_hip_pitch = float(joint_pos[11])
-        right_hip_pitch = float(joint_pos[17])
-
-        left_ankle_roll = float(joint_pos[16])
-        right_ankle_roll = float(joint_pos[22])
-
-        max_leg_roll = 0.2  # 防止劈叉姿势
-        split_penalty = -0.8 * max(0.0, (-left_hip_roll + right_hip_roll - 2 * max_leg_roll) / max_leg_roll)
-        left_hip_yaw = float(joint_pos[13])
-        right_hip_yaw = float(joint_pos[19])
-
-        min_leg_separation = 0.05  # 最小腿间距（防止贴得太近）
-        # 惩罚腿过分靠拢（内收）- 基于两腿间距
-        leg_separation = -left_hip_roll + right_hip_roll
-        inward_penalty = -0.25 * max(0.0, (min_leg_separation - leg_separation) / min_leg_separation)
-        
-        
-        # 脚踝roll角度检测：防止过度外翻或内翻
-        max_ankle_roll = 0.15  # 最大允许的脚踝roll角度
-        
-        # 惩罚脚踝过度外翻/内翻（绝对值过大）
-        ankle_roll_penalty = -0.5 * max(0.0, (abs(left_ankle_roll) + abs(right_ankle_roll) - 2 * max_ankle_roll) / max_ankle_roll)
-        
-        # 惩罚两脚踝roll方向相反（不稳定姿势）
-        ankle_roll_cross_penalty = -0.3 * max(0.0, -(left_ankle_roll * right_ankle_roll))
-
-       # 分别惩罚左右大腿过度转动
-        max_hip_yaw = 0.4  # 最大允许的yaw角度
-        left_hip_yaw_penalty = -0.4 * max(0.0, abs(left_hip_yaw) - max_hip_yaw)
-        right_hip_yaw_penalty = -0.4 * max(0.0, abs(right_hip_yaw) - max_hip_yaw)
-        # 智能交叉腿惩罚：只在站立时惩罚，转身时允许交叉腿
-        yaw_rate = float(np.deg2rad(robot.gyroscope[2]))
-        yaw_rate_abs = abs(yaw_rate)
-
-        # 当转身速度较小时才惩罚交叉腿（站立状态）
-        cross_leg_gate = max(0.0, 1.0 - yaw_rate_abs / math.radians(8.0))
-        hip_yaw_cross_penalty = -1.0 * cross_leg_gate * max(0.0, -(left_hip_yaw * right_hip_yaw)) if left_hip_yaw > 0 and right_hip_yaw < 0 else 0.0
-
-        # Torso-lower-body linkage: reward coordinated turning, punish waist-only spinning.
-        waist_speed = abs(float(joint_speed_rad[10]))
-        lower_body_speed = float(np.mean(np.abs(joint_speed_rad[11:23])))
-        lower_body_follow_ratio = lower_body_speed / (waist_speed + 1e-4)
-        linkage_reward = 0.24 * min(1.0, lower_body_follow_ratio) * min(1.0, waist_speed / 1.2)
-        waist_only_turn_penalty = -0.20 * max(0.0, waist_speed - 1.35 * lower_body_speed)
-
-        # Extra posture linkage in yaw joints to avoid decoupled torso twist.
-        waist_yaw = abs(float(joint_pos_rad[10]))
-        hip_yaw_mean = 0.5 * (abs(float(joint_pos_rad[13])) + abs(float(joint_pos_rad[19])))
-        yaw_link_reward = 0.12 * math.exp(-abs(waist_yaw - hip_yaw_mean) / 0.22)
-
-        target_height = self.initial_height
-        height_error =  height - target_height
-        height_error = height - target_height
-        
-        height_penalty = -(math.exp(12*abs(height_error))-1) if height_error > 0.04 else 0
-
-        # # 在 compute_reward 开头附近，添加高度变化率计算
-        # if not hasattr(self, 'last_height'):
-        #     self.last_height = height
-        #     self.last_height_time = self.step_counter  # 可选，用于时间间隔
-        # height_rate = height - self.last_height  # 正为上升，负为下降
-        # self.last_height = height
-
-        # 惩罚高度下降（负变化率）
-        # height_down_penalty = -5.0 * max(0, -height_rate)  # 系数可调，-height_rate 为正表示下降幅度
-
-        # # 在 compute_reward 中
-        # if self.step_counter > 50:
-        #     avg_prev_action = np.mean(self.prev_action_history, axis=0)
-        #     novelty = float(np.linalg.norm(action - avg_prev_action))
-        #     exploration_bonus = 0.05 * novelty
-        # else:
-        #     exploration_bonus = 0
-
-        # self.prev_action_history[self.history_idx] = action
-        # self.history_idx = (self.history_idx + 1) % 50
-
-
-        total = (
-            # progress_reward  + 
-                alive_bonus + 
-                head_toward_bonus +
-                heading_progress_reward +
-                # lateral_penalty +
-                # action_penalty +
-                smoothness_penalty +
-                posture_penalty
-                + ang_vel_penalty
-                + height_penalty
-                + ankle_roll_penalty
-                + ankle_roll_cross_penalty
-                + split_penalty
-                + inward_penalty
-                # + leg_proximity_penalty
-                # + left_hip_yaw_penalty
-                # + right_hip_yaw_penalty
-                # + hip_yaw_cross_penalty
-                + position_penalty
-                # + linkage_reward
-                # + waist_only_turn_penalty
-                # + yaw_link_reward
-                # + stance_collapse_penalty
-                # + hip_yaw_yaw_cross_penalty
-                # + stance_collapse_penalty
-                #  + cross_leg_penalty
-                #  + exploration_bonus
-                # + height_down_penalty
-                 )
-        # print(height_error, height_penalty)
-        
-        now = time.time()
-        if self.reward_debug_interval_sec > 0 and now - self._reward_debug_last_time >= self.reward_debug_interval_sec:
-            self._reward_debug_last_time = now
-            self._reward_debug_steps_left = max(1, self.reward_debug_burst_steps)
-
-        if self._reward_debug_steps_left > 0:
-            self._reward_debug_steps_left -= 1
-            self.debug_log(
-                f"height_penalty:{height_penalty:.4f},"
-                f"smoothness_penalty:{smoothness_penalty:.4f},"
-                f"posture_penalty:{posture_penalty:.4f},"
-                f"heading_progress_reward:{heading_progress_reward:.4f},"
-                # f"stance_collapse_penalty:{stance_collapse_penalty:.4f},"
-                # f"cross_leg_penalty:{cross_leg_penalty:.4f},"
-                f"ang_vel_penalty:{ang_vel_penalty:.4f},"
-                f"split_penalty:{split_penalty:.4f},"
-                f"ankle_roll_penalty:{ankle_roll_penalty:.4f},"
-                f"ankle_roll_cross_penalty:{ankle_roll_cross_penalty:.4f},"
-                f"left_hip_yaw_penalty:{left_hip_yaw_penalty:.4f},"
-                f"right_hip_yaw_penalty:{right_hip_yaw_penalty:.4f},"
-                f"hip_yaw_cross_penalty:{hip_yaw_cross_penalty:.4f},"
-                f"inward_penalty:{inward_penalty:.4f},"
-                f"position_penalty:{position_penalty:.4f},"
-                # f"linkage_reward:{linkage_reward:.4f},"
-                # f"waist_only_turn_penalty:{waist_only_turn_penalty:.4f},"
-                # f"yaw_link_reward:{yaw_link_reward:.4f}"
-                # f"leg_proximity_penalty:{leg_proximity_penalty:.4f},"
-
-                # f"stance_collapse_penalty:{stance_collapse_penalty:.4f},"
-                # f"hip_yaw_yaw_cross_penalty:{hip_yaw_yaw_cross_penalty:.4f},"
-                #   f"height_down_penalty:{height_down_penalty:.4f}",
-                #   f"exploration_bonus:{exploration_bonus:.4f}"
-                f"alive_bonus:{alive_bonus:.4f},"
-                f"abs_yaw_error:{abs_yaw_error:.4f}"
-                f"total:{total:.4f}"
-                )
-        # print(f"abs_yaw_error:{abs_yaw_error:.4f}")
-        return total
-
-
-
-    def step(self, action):
-
-        r = self.Player.robot
-        max_action_delta =  0.5# Limit how much the action can change from the previous step to encourage smoother motions.
-        if self.previous_action is not None:
-            action = np.clip(action, self.previous_action - max_action_delta, self.previous_action + max_action_delta)
-        action[0:2] = 0
-        action[3] = 4
-        action[7] = -4
-        action[2] = 0
-        action[6] = 0
-        action[4] = 0
-        action[5] = -5
-        action[8] = 0
-        action[9] = 5
-        action[10] = 0
-        action[11] = np.clip(action[11], -0.4, 0.4)
-        action[17] = np.clip(action[17], -0.4, 0.4)
-        # action[12] = -1.0
-        # action[18] = 1.0
-        # action[13] = -1.0
-        # action[19] = 1.0
-        self.previous_action = action.copy()
-
-        self.target_joint_positions = (
-                # self.joint_nominal_position +
-                 self.scaling_factor * action
-        )
-        self.target_joint_positions *= self.train_sim_flip
-
-        for idx, target in enumerate(self.target_joint_positions):
-            r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=80, kd=4.67
-            )
-
-        self.previous_action = action.copy()
-
-        self.sync()  # run simulation step
-        self.step_counter += 1
-
-        if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
-            self.debug_joint_status()
-
-        current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
-
-        if self.step_counter % 10 == 0:
-            self.previous_pos = current_pos.copy()
-
-        # Compute reward based on movement from previous step
-        reward = self.compute_reward(self.previous_pos, current_pos, action)
-
-        self.last_action_for_reward = action.copy()
-
-        # Fall detection and penalty
-        is_fallen = self.Player.world.global_position[2] < 0.55
-
-        # terminal state: the robot is falling or timeout
-        terminated = is_fallen or self.step_counter > 800 or self.route_completed
-        truncated = False
-
-        return self.observe(), reward, terminated, truncated, {}
-
-
-class Train(Train_Base):
-    def __init__(self, script) -> None:
-        super().__init__(script)
-
-    def train(self, args):
-
-        # --------------------------------------- Learning parameters
-        n_envs = int(os.environ.get("GYM_CPU_N_ENVS", "20"))
-        if n_envs < 1:
-            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
-        server_warmup_sec = float(os.environ.get("GYM_CPU_SERVER_WARMUP_SEC", "3.0"))
-        n_steps_per_env = int(os.environ.get("GYM_CPU_TRAIN_STEPS_PER_ENV", "512"))  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = int(os.environ.get("GYM_CPU_TRAIN_BATCH_SIZE", "512"))  # should be a factor of (n_steps_per_env * n_envs)
-        total_steps = 30000000
-        learning_rate = float(os.environ.get("GYM_CPU_TRAIN_LR", "3e-4"))
-        folder_name = f'Turn_R{self.robot_type}'
-        model_path = f'./scripts/gyms/logs/{folder_name}/'
-
-        print(f"Model path: {model_path}")
-        print(f"Using {n_envs} parallel environments")
-
-        # --------------------------------------- Run algorithm
-        def init_env(i_env, monitor=False):
-            def thunk():
-                env = WalkEnv(self.ip, self.server_p + i_env)
-                if monitor:
-                    env = Monitor(env)
-                return env
-
-            return thunk
-
-        server_log_dir = os.path.join(model_path, "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1, no_render=True, no_realtime=True)  # include 1 extra server for testing
-
-        # Wait for servers to start
-        print(f"Starting {n_envs + 1} rcssservermj servers...")
-        if server_warmup_sec > 0:
-            print(f"Waiting {server_warmup_sec:.1f}s for server warmup...")
-            sleep(server_warmup_sec)
-        print("Servers started, creating environments...")
-
-        env = SubprocVecEnv([init_env(i, monitor=True) for i in range(n_envs)], start_method="spawn")
-        # Use single-process eval env to avoid extra subprocess fragility during callback evaluation.
-        eval_env = DummyVecEnv([init_env(n_envs, monitor=True)])
-
-        try:
-            # Custom policy network architecture
-            policy_kwargs = dict(
-                net_arch=dict(
-                    pi=[512, 256, 128],  # Policy network: 3 layers
-                    vf=[512, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
-            )
-
-            if "model_file" in args:  # retrain
-                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
-                                 batch_size=minibatch_size, learning_rate=learning_rate)
-            else:  # train new model
-                model = PPO(
-                    "MlpPolicy",
-                    env=env,
-                    verbose=1,
-                    n_steps=n_steps_per_env,
-                    batch_size=minibatch_size,
-                    learning_rate=learning_rate,
-                    device="cpu",
-                    policy_kwargs=policy_kwargs,
-                    ent_coef=float(os.environ.get("GYM_CPU_TRAIN_ENT_COEF", "0.05")),  # Entropy coefficient for exploration
-                    clip_range=float(os.environ.get("GYM_CPU_TRAIN_CLIP_RANGE", "0.2")),  # PPO clipping parameter
-                    gae_lambda=0.95,  # GAE lambda
-                    gamma=float(os.environ.get("GYM_CPU_TRAIN_GAMMA", "0.95")), # Discount factor
-                    # target_kl=0.03,
-                    n_epochs=int(os.environ.get("GYM_CPU_TRAIN_EPOCHS", "5")),
-                    tensorboard_log=f"./scripts/gyms/logs/{folder_name}/tensorboard/"
-                )
-
-            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
-                                          eval_freq=n_steps_per_env * 20, save_freq=n_steps_per_env * 20, eval_eps=7,
-                                          backup_env_file=__file__)
-        except KeyboardInterrupt:
-            sleep(1)  # wait for child processes
-            print("\nctrl+c pressed, aborting...\n")
-            servers.kill()
-            return
-
-        env.close()
-        eval_env.close()
-        servers.kill()
-
-    def test(self, args):
-
-        # Uses different server and monitor ports
-        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        test_no_render = os.environ.get("GYM_CPU_TEST_NO_RENDER", "0") == "1"
-        test_no_realtime = os.environ.get("GYM_CPU_TEST_NO_REALTIME", "0") == "1"
-
-        server = Train_Server(
-            self.server_p - 1,
-            self.monitor_p,
-            1,
-            no_render=test_no_render,
-            no_realtime=test_no_realtime,
-        )
-        env = WalkEnv(self.ip, self.server_p - 1)
-        model = PPO.load(args["model_file"], env=env)
-
-        try:
-            self.export_model(args["model_file"], args["model_file"] + ".pkl",
-                              False)  # Export to pkl to create custom behavior
-            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
-        except KeyboardInterrupt:
-            print()
-
-        env.close()
-        server.kill()
-
-
-if __name__ == "__main__":
-    from types import SimpleNamespace
-
-    # 创建默认参数
-    script_args = SimpleNamespace(
-        args=SimpleNamespace(
-            i='127.0.0.1',  # Server IP
-            p=3100,  # Server port
-            m=3200,  # Monitor port
-            r=0,  # Robot type
-            t='Gym',  # Team name
-            u=1  # Uniform number
-        )
-    )
-
-    trainer = Train(script_args)
-
-    run_mode = os.environ.get("GYM_CPU_MODE", "train").strip().lower()
-
-    if run_mode == "test":
-        test_model_file = os.environ.get("GYM_CPU_TEST_MODEL", "scripts/gyms/logs/Turn_R0_004/best_model.zip")
-        test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Turn_R0_004/")
-        trainer.test({"model_file": test_model_file, "folder_dir": test_folder})
-    else:
-        retrain_model = os.environ.get("GYM_CPU_TRAIN_MODEL", "").strip()
-        if retrain_model:
-            trainer.train({"model_file": retrain_model})
-        else:
-            trainer.train({})

scripts/gyms/logs/Turn_R0_014/Walk.py

@@ -1,853 +0,0 @@
-import os
-import numpy as np
-import math
-import time
-from time import sleep
-from random import random
-from random import uniform
-from itertools import count
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.monitor import Monitor
-from stable_baselines3.common.vec_env import SubprocVecEnv, DummyVecEnv
-
-import gymnasium as gym
-from gymnasium import spaces
-
-from scripts.commons.Train_Base import Train_Base
-from scripts.commons.Server import Server as Train_Server
-
-from agent.base_agent import Base_Agent
-from utils.math_ops import MathOps
-
-from scipy.spatial.transform import Rotation as R
-
-'''
-Objective:
-Learn how to run forward using step primitive
-----------
-- class Basic_Run: implements an OpenAI custom gym
-- class Train:  implements algorithms to train a new model or test an existing model
-'''
-
-
-class WalkEnv(gym.Env):
-    def __init__(self, ip, server_p) -> None:
-
-        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
-        self.Player = player = Base_Agent(
-            team_name="Gym",
-            number=1,
-            host=ip,
-            port=server_p
-        )
-        self.robot_type = self.Player.robot
-        self.step_counter = 0  # to limit episode size
-        self.force_play_on = True
-
-        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
-        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
-        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
-        self.isfallen = False
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.debug_every_n_steps = 5
-        self.enable_debug_joint_status = False
-        self.reward_debug_interval_sec = float(os.environ.get("GYM_CPU_REWARD_DEBUG_INTERVAL_SEC", "600"))
-        self.reward_debug_burst_steps = int(os.environ.get("GYM_CPU_REWARD_DEBUG_BURST_STEPS", "10"))
-        self._reward_debug_last_time = time.time()
-        self._reward_debug_steps_left = 0
-        self.calibrate_nominal_from_neutral = True
-        self.auto_calibrate_train_sim_flip = True
-        self.nominal_calibrated_once = False
-        self.flip_calibrated_once = False
-        self._target_hz = 0.0
-        self._target_dt = 0.0
-        self._last_sync_time = None
-        target_hz_env = 0
-        if target_hz_env:
-            try:
-                self._target_hz = float(target_hz_env)
-            except ValueError:
-                self._target_hz = 0.0
-        if self._target_hz > 0.0:
-            self._target_dt = 1.0 / self._target_hz
-
-        # State space
-        # 原始观测大小: 78
-        obs_size = 78
-        self.obs = np.zeros(obs_size, np.float32)
-        self.observation_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(obs_size,),
-            dtype=np.float32
-        )
-
-        action_dim = len(self.Player.robot.ROBOT_MOTORS)
-        self.no_of_actions = action_dim
-        self.action_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(action_dim,),
-            dtype=np.float32
-        )
-
-        # 中立姿态
-        self.joint_nominal_position = np.array(
-            [
-                0.0,  # 0: Head_yaw (he1)
-                0.0,  # 1: Head_pitch (he2)
-                0.0,  # 2: Left_Shoulder_Pitch (lae1)
-                0.0,  # 3: Left_Shoulder_Roll (lae2)
-                0.0,  # 4: Left_Elbow_Pitch (lae3)
-                0.0,  # 5: Left_Elbow_Yaw (lae4)
-                0.0,  # 6: Right_Shoulder_Pitch (rae1)
-                0.0,  # 7: Right_Shoulder_Roll (rae2)
-                0.0,  # 8: Right_Elbow_Pitch (rae3)
-                0.0,  # 9: Right_Elbow_Yaw (rae4)
-                0.0,  # 10: Waist (te1)
-                0.0,  # 11: Left_Hip_Pitch (lle1)
-                0.0,  # 12: Left_Hip_Roll (lle2)
-                1.0,  # 13: Left_Hip_Yaw (lle3)
-                0.0,  # 14: Left_Knee_Pitch (lle4)
-                0.0,  # 15: Left_Ankle_Pitch (lle5)
-                0.0,  # 16: Left_Ankle_Roll (lle6)
-                0.0,  # 17: Right_Hip_Pitch (rle1)
-                0.0,  # 18: Right_Hip_Roll (rle2)
-                1.0,  # 19: Right_Hip_Yaw (rle3)
-                0.0,  # 20: Right_Knee_Pitch (rle4)
-                0.0,  # 21: Right_Ankle_Pitch (rle5)
-                0.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-        self.joint_nominal_position = np.zeros(self.no_of_actions)
-        self.train_sim_flip = np.array(
-            [
-                1.0,  # 0: Head_yaw (he1)
-                -1.0,  # 1: Head_pitch (he2)
-                1.0,  # 2: Left_Shoulder_Pitch (lae1)
-                -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                -1.0,  # 4: Left_Elbow_Pitch (lae3)
-                1.0,  # 5: Left_Elbow_Yaw (lae4)
-                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                -1.0,  # 7: Right_Shoulder_Roll (rae2)
-                1.0,  # 8: Right_Elbow_Pitch (rae3)
-                1.0,  # 9: Right_Elbow_Yaw (rae4)
-                1.0,  # 10: Waist (te1)
-                1.0,  # 11: Left_Hip_Pitch (lle1)
-                -1.0,  # 12: Left_Hip_Roll (lle2)
-                -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,  # 14: Left_Knee_Pitch (lle4)
-                1.0,  # 15: Left_Ankle_Pitch (lle5)
-                -1.0,  # 16: Left_Ankle_Roll (lle6)
-                -1.0,  # 17: Right_Hip_Pitch (rle1)
-                -1.0,  # 18: Right_Hip_Roll (rle2)
-                -1.0,  # 19: Right_Hip_Yaw (rle3)
-                -1.0,  # 20: Right_Knee_Pitch (rle4)
-                -1.0,  # 21: Right_Ankle_Pitch (rle5)
-                -1.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-
-        self.scaling_factor = 0.3
-        # self.scaling_factor = 1
-
-        # Encourage a minimum lateral stance so the policy avoids feet overlap.
-        self.min_stance_rad = 0.10
-
-        # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = False
-        self.reset_beam_yaw_range_deg = float(os.environ.get("GYM_CPU_RESET_BEAM_YAW_RANGE_DEG", "180"))
-        self.reset_target_bearing_range_deg = float(os.environ.get("GYM_CPU_RESET_TARGET_BEARING_RANGE_DEG", "120"))
-        self.reset_target_distance_min = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MIN", "1.2"))
-        self.reset_target_distance_max = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MAX", "2.8"))
-        if self.reset_target_distance_min > self.reset_target_distance_max:
-            self.reset_target_distance_min, self.reset_target_distance_max = (
-                self.reset_target_distance_max,
-                self.reset_target_distance_min,
-            )
-        self.reset_joint_noise_rad = 0.025
-        self.reset_perturb_steps = 4
-        self.reset_recover_steps = 8
-
-        self.reward_smoothness_scale = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_SCALE", "0.06"))
-        self.reward_smoothness_cap = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_CAP", "0.45"))
-        self.reward_head_toward_bonus = float(os.environ.get("GYM_CPU_REWARD_HEAD_TOWARD_BONUS", "1"))
-
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Track previous position
-        self.last_yaw_error = None
-        self.Player.server.connect()
-        # sleep(2.0)  # Longer wait for connection to establish completely
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-        self.start_time = time.time()
-
-    def _reconnect_server(self):
-        try:
-            self.Player.server.shutdown()
-        except Exception:
-            pass
-
-        self.Player.server.connect()
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-
-    def _safe_receive_world_update(self, retries=1):
-        last_exc = None
-        for attempt in range(retries + 1):
-            try:
-                self.Player.server.receive()
-                self.Player.world.update()
-                return
-            except (ConnectionResetError, OSError) as exc:
-                last_exc = exc
-                if attempt >= retries:
-                    raise
-                self._reconnect_server()
-        if last_exc is not None:
-            raise last_exc
-
-    def debug_log(self, message):
-        print(message)
-        try:
-            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
-            with open(log_path, "a", encoding="utf-8") as f:
-                f.write(message + "\n")
-        except OSError:
-            pass
-
-    @staticmethod
-    def _wrap_to_pi(angle_rad: float) -> float:
-        return (angle_rad + math.pi) % (2.0 * math.pi) - math.pi
-
-    def observe(self, init=False):
-
-        """获取当前观测值"""
-        robot = self.Player.robot
-        world = self.Player.world
-
-        # Safety check: ensure data is available
-
-        # 计算目标速度
-        raw_target = self.target_position - world.global_position[:2]
-        velocity = MathOps.rotate_2d_vec(
-            raw_target,
-            -robot.global_orientation_euler[2],
-            is_rad=False
-        )
-
-        # 计算相对方向
-        rel_orientation = MathOps.vector_angle(velocity) * 0.3
-        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
-
-        velocity = np.concatenate([velocity, np.array([rel_orientation])])
-        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
-        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
-
-        # 关节状态
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        qpos_qvel_previous_action = np.concatenate([
-            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
-            radian_joint_speeds / 110.0 * self.train_sim_flip,
-            self.previous_action / 10.0,
-        ])
-
-        # 角速度
-        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
-
-        # 投影的重力方向
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-
-        # 组合观测
-        observation = np.concatenate([
-            qpos_qvel_previous_action,
-            ang_vel,
-            velocity,
-            projected_gravity,
-        ])
-
-        observation = np.clip(observation, -10.0, 10.0)
-        return observation.astype(np.float32)
-
-    def sync(self):
-        ''' Run a single simulation step '''
-        self._safe_receive_world_update(retries=1)
-        self.Player.robot.commit_motor_targets_pd()
-        self.Player.server.send()
-        if self._target_dt > 0.0:
-            now = time.time()
-            if self._last_sync_time is None:
-                self._last_sync_time = now
-                return
-            elapsed = now - self._last_sync_time
-            remaining = self._target_dt - elapsed
-            if remaining > 0.0:
-                time.sleep(remaining)
-                now = time.time()
-            self._last_sync_time = now
-
-    def debug_joint_status(self):
-        robot = self.Player.robot
-        actual_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        target_joint_positions = getattr(
-            self,
-            'target_joint_positions',
-            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
-        )
-        joint_error = actual_joint_positions - target_joint_positions
-        leg_slice = slice(11, None)
-
-        self.debug_log(
-            "[WalkDebug] "
-            f"step={self.step_counter} "
-            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
-            f"target_xy={np.round(self.target_position, 3).tolist()} "
-            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
-            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
-            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
-            f"fallen={self.Player.world.global_position[2] < 0.3}"
-        )
-        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
-
-    def reset(self, seed=None, options=None):
-        '''
-        Reset and stabilize the robot
-        Note: for some behaviors it would be better to reduce stabilization or add noise
-        '''
-        r = self.Player.robot
-        super().reset(seed=seed)
-        if seed is not None:
-            np.random.seed(seed)
-
-        target_distance = np.random.uniform(self.reset_target_distance_min, self.reset_target_distance_max)
-        target_bearing_deg = np.random.uniform(-self.reset_target_bearing_range_deg, self.reset_target_bearing_range_deg)
-
-        self.step_counter = 0
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
-        self.last_yaw_error = None
-        self.walk_cycle_step = 0
-        self._reward_debug_steps_left = 0
-
-        # 随机 beam 目标位置和朝向，增加训练多样性
-        beam_x = (random() - 0.5) * 10
-        beam_y = (random() - 0.5) * 10
-        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
-
-        for _ in range(5):
-            self._safe_receive_world_update(retries=2)
-            self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
-            self.Player.server.send()
-
-        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
-        finished_count = 0
-        for _ in range(50):
-            finished = self.Player.skills_manager.execute("Neutral")
-            self.sync()
-            if finished:
-                finished_count += 1
-                if finished_count >= 20:  # 假设需要连续20次完成才算成功
-                    break
-
-        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-            # Perturb waist + lower body only (10:), keep head/arms stable.
-            perturb_action[10:] = np.random.uniform(
-                -self.reset_joint_noise_rad,
-                self.reset_joint_noise_rad,
-                size=(self.no_of_actions - 10,)
-            )
-
-            for _ in range(self.reset_perturb_steps):
-                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-            for i in range(self.reset_recover_steps):
-                # Linearly fade perturbation to help policy start from near-neutral.
-                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-        # memory variables
-        self.sync()  
-        self.initial_position = np.array(self.Player.world.global_position[:2])
-        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions, np.float32)
-        # Randomize global target bearing so policy must learn to rotate toward it first.
-        heading_deg = float(r.global_orientation_euler[2])
-        target_offset = MathOps.rotate_2d_vec(
-            np.array([target_distance, 0.0]),
-            heading_deg + target_bearing_deg,
-            is_rad=False,
-        )
-        point1 = self.initial_position + target_offset
-        self.point_list = [point1]
-        self.target_position = self.point_list[self.waypoint_index]
-        self.initial_height = self.Player.world.global_position[2]
-
-        return self.observe(True), {}
-
-    def render(self, mode='human', close=False):
-        return
-
-    
-    def compute_reward(self, previous_pos, current_pos, action):
-        height = float(self.Player.world.global_position[2])
-        robot = self.Player.robot
-
-
-        joint_pos_rad = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        joint_speed_rad = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-        tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        ang_vel = np.deg2rad(robot.gyroscope)
-        rp_ang_vel_mag = float(np.linalg.norm(ang_vel[:2]))
-
-        is_fallen = height < 0.55
-        if is_fallen:
-            # remain = max(0, 800 - self.step_counter)
-            # return -8.0 - 0.01 * remain
-            return -20.0
-        
-
-        if np.linalg.norm(current_pos - previous_pos) > 0.005:
-            position_penalty = -3 * float(np.linalg.norm(current_pos - previous_pos))
-        else:
-            position_penalty = 0.0
-
-
-        # Turn-to-target shaping.
-        to_target = self.target_position - current_pos
-        dist_to_target = float(np.linalg.norm(to_target))
-        if dist_to_target > 1e-6:
-            target_yaw = math.atan2(float(to_target[1]), float(to_target[0]))
-        else:
-            target_yaw = 0.0
-
-        robot_yaw = math.radians(float(robot.global_orientation_euler[2]))
-        yaw_error = target_yaw - robot_yaw
-
-        # Main heading objective: face the target direction.
-        # heading_align_reward = 1.0 * math.cos(yaw_error)
-
-        abs_yaw_error = abs(yaw_error)
-        alive_bonus = 2.0 * max(0.0, 1.0 - abs_yaw_error / math.pi)
-        head_toward_bonus = self.reward_head_toward_bonus if abs_yaw_error < math.radians(4.0) else 0.0
-
-        if self.last_yaw_error is None:
-            heading_progress_reward = 0.0
-        else:
-            prev_abs_yaw_error = abs(self.last_yaw_error)
-            yaw_err_delta = prev_abs_yaw_error - abs_yaw_error
-            progress_gate = 1.0 if abs_yaw_error > math.radians(4.0) else 0.0
-            heading_progress_reward =   progress_gate * yaw_err_delta
-            heading_progress_reward = float(np.clip(heading_progress_reward, -1, 1))
-        self.last_yaw_error = yaw_error
-
-        # action_penalty = -0.01 * float(np.linalg.norm(action))
-        smoothness_penalty = -0.05 * float(np.linalg.norm(action - self.last_action_for_reward))
-
-        posture_penalty = -0.6 * tilt_mag
-        # Penalize roll/pitch rotational shake but do not penalize yaw turning directly.
-        ang_vel_penalty = -0.06 * rp_ang_vel_mag
-
-        joint_pos = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        ) * self.train_sim_flip
-        left_hip_roll = float(joint_pos[12])
-        right_hip_roll = float(joint_pos[18])
-        left_hip_pitch = float(joint_pos[11])
-        right_hip_pitch = float(joint_pos[17])
-
-        left_ankle_roll = float(joint_pos[16])
-        right_ankle_roll = float(joint_pos[22])
-
-        max_leg_roll = 0.2  # 防止劈叉姿势
-        split_penalty = -0.8 * max(0.0, (-left_hip_roll + right_hip_roll - 2 * max_leg_roll) / max_leg_roll)
-        left_hip_yaw = float(joint_pos[13])
-        right_hip_yaw = float(joint_pos[19])
-
-        min_leg_separation = 0.05  # 最小腿间距（防止贴得太近）
-        # 惩罚腿过分靠拢（内收）- 基于两腿间距
-        leg_separation = -left_hip_roll + right_hip_roll
-        inward_penalty = -0.25 * max(0.0, (min_leg_separation - leg_separation) / min_leg_separation)
-        
-        
-        # 脚踝roll角度检测：防止过度外翻或内翻
-        max_ankle_roll = 0.15  # 最大允许的脚踝roll角度
-        
-        # 惩罚脚踝过度外翻/内翻（绝对值过大）
-        ankle_roll_penalty = -0.5 * max(0.0, (abs(left_ankle_roll) + abs(right_ankle_roll) - 2 * max_ankle_roll) / max_ankle_roll)
-        
-        # 惩罚两脚踝roll方向相反（不稳定姿势）
-        ankle_roll_cross_penalty = -0.3 * max(0.0, -(left_ankle_roll * right_ankle_roll))
-
-       # 分别惩罚左右大腿过度转动
-        max_hip_yaw = 0.5  # 最大允许的yaw角度
-        left_hip_yaw_penalty = -0.4 * max(0.0, abs(left_hip_yaw) - max_hip_yaw)
-        right_hip_yaw_penalty = -0.4 * max(0.0, abs(right_hip_yaw) - max_hip_yaw)
-        # 智能交叉腿惩罚：只在站立时惩罚，转身时允许交叉腿
-        yaw_rate = float(np.deg2rad(robot.gyroscope[2]))
-        yaw_rate_abs = abs(yaw_rate)
-
-        # 当转身速度较小时才惩罚交叉腿（站立状态）
-        cross_leg_gate = max(0.0, 1.0 - yaw_rate_abs / math.radians(8.0))
-        hip_yaw_cross_penalty = -1.0 * cross_leg_gate * max(0.0, -(left_hip_yaw * right_hip_yaw)) if left_hip_yaw > 0 and right_hip_yaw < 0 else 0.0
-
-        # Torso-lower-body linkage: reward coordinated turning, punish waist-only spinning.
-        waist_speed = abs(float(joint_speed_rad[10]))
-        lower_body_speed = float(np.mean(np.abs(joint_speed_rad[11:23])))
-        lower_body_follow_ratio = lower_body_speed / (waist_speed + 1e-4)
-        linkage_reward = 0.24 * min(1.0, lower_body_follow_ratio) * min(1.0, waist_speed / 1.2)
-        waist_only_turn_penalty = -0.20 * max(0.0, waist_speed - 1.35 * lower_body_speed)
-
-        # Extra posture linkage in yaw joints to avoid decoupled torso twist.
-        waist_yaw = abs(float(joint_pos_rad[10]))
-        hip_yaw_mean = 0.5 * (abs(float(joint_pos_rad[13])) + abs(float(joint_pos_rad[19])))
-        yaw_link_reward = 0.12 * math.exp(-abs(waist_yaw - hip_yaw_mean) / 0.22)
-
-        target_height = self.initial_height
-        height_error =  height - target_height
-        height_error = height - target_height
-        
-        height_penalty = -(math.exp(12*abs(height_error))-1) if height_error > 0.04 else 0
-
-        # # 在 compute_reward 开头附近，添加高度变化率计算
-        # if not hasattr(self, 'last_height'):
-        #     self.last_height = height
-        #     self.last_height_time = self.step_counter  # 可选，用于时间间隔
-        # height_rate = height - self.last_height  # 正为上升，负为下降
-        # self.last_height = height
-
-        # 惩罚高度下降（负变化率）
-        # height_down_penalty = -5.0 * max(0, -height_rate)  # 系数可调，-height_rate 为正表示下降幅度
-
-        # # 在 compute_reward 中
-        # if self.step_counter > 50:
-        #     avg_prev_action = np.mean(self.prev_action_history, axis=0)
-        #     novelty = float(np.linalg.norm(action - avg_prev_action))
-        #     exploration_bonus = 0.05 * novelty
-        # else:
-        #     exploration_bonus = 0
-
-        # self.prev_action_history[self.history_idx] = action
-        # self.history_idx = (self.history_idx + 1) % 50
-
-
-        total = (
-            # progress_reward  + 
-                alive_bonus + 
-                head_toward_bonus +
-                heading_progress_reward +
-                # lateral_penalty +
-                # action_penalty +
-                smoothness_penalty +
-                posture_penalty
-                + ang_vel_penalty
-                + height_penalty
-                + ankle_roll_penalty
-                + ankle_roll_cross_penalty
-                + split_penalty
-                + inward_penalty
-                # + leg_proximity_penalty
-                + left_hip_yaw_penalty
-                + right_hip_yaw_penalty
-                + hip_yaw_cross_penalty
-                + position_penalty
-                # + linkage_reward
-                # + waist_only_turn_penalty
-                # + yaw_link_reward
-                # + stance_collapse_penalty
-                # + hip_yaw_yaw_cross_penalty
-                # + stance_collapse_penalty
-                #  + cross_leg_penalty
-                #  + exploration_bonus
-                # + height_down_penalty
-                 )
-        # print(height_error, height_penalty)
-        
-        now = time.time()
-        if self.reward_debug_interval_sec > 0 and now - self._reward_debug_last_time >= self.reward_debug_interval_sec:
-            self._reward_debug_last_time = now
-            self._reward_debug_steps_left = max(1, self.reward_debug_burst_steps)
-
-        if self._reward_debug_steps_left > 0:
-            self._reward_debug_steps_left -= 1
-            self.debug_log(
-                f"height_penalty:{height_penalty:.4f},"
-                f"smoothness_penalty:{smoothness_penalty:.4f},"
-                f"posture_penalty:{posture_penalty:.4f},"
-                f"heading_progress_reward:{heading_progress_reward:.4f},"
-                # f"stance_collapse_penalty:{stance_collapse_penalty:.4f},"
-                # f"cross_leg_penalty:{cross_leg_penalty:.4f},"
-                f"ang_vel_penalty:{ang_vel_penalty:.4f},"
-                f"split_penalty:{split_penalty:.4f},"
-                f"ankle_roll_penalty:{ankle_roll_penalty:.4f},"
-                f"ankle_roll_cross_penalty:{ankle_roll_cross_penalty:.4f},"
-                f"left_hip_yaw_penalty:{left_hip_yaw_penalty:.4f},"
-                f"right_hip_yaw_penalty:{right_hip_yaw_penalty:.4f},"
-                f"hip_yaw_cross_penalty:{hip_yaw_cross_penalty:.4f},"
-                f"inward_penalty:{inward_penalty:.4f},"
-                f"position_penalty:{position_penalty:.4f},"
-                # f"linkage_reward:{linkage_reward:.4f},"
-                # f"waist_only_turn_penalty:{waist_only_turn_penalty:.4f},"
-                # f"yaw_link_reward:{yaw_link_reward:.4f}"
-                # f"leg_proximity_penalty:{leg_proximity_penalty:.4f},"
-
-                # f"stance_collapse_penalty:{stance_collapse_penalty:.4f},"
-                # f"hip_yaw_yaw_cross_penalty:{hip_yaw_yaw_cross_penalty:.4f},"
-                #   f"height_down_penalty:{height_down_penalty:.4f}",
-                #   f"exploration_bonus:{exploration_bonus:.4f}"
-                f"alive_bonus:{alive_bonus:.4f},"
-                f"abs_yaw_error:{abs_yaw_error:.4f}"
-                f"total:{total:.4f}"
-                )
-        # print(f"abs_yaw_error:{abs_yaw_error:.4f}")
-        return total
-
-
-
-    def step(self, action):
-
-        r = self.Player.robot
-        max_action_delta =  0.5# Limit how much the action can change from the previous step to encourage smoother motions.
-        if self.previous_action is not None:
-            action = np.clip(action, self.previous_action - max_action_delta, self.previous_action + max_action_delta)
-        action[0:2] = 0
-        action[3] = 4
-        action[7] = -4
-        action[2] = 0
-        action[6] = 0
-        action[4] = 0
-        action[5] = -5
-        action[8] = 0
-        action[9] = 5
-        action[10] = 0
-        action[11] = np.clip(action[11], -0.7, 0.7)
-        action[17] = np.clip(action[17], -0.7, 0.7)
-        # action[12] = -1.0
-        # action[18] = 1.0
-        # action[13] = -1.0
-        # action[19] = 1.0
-        self.previous_action = action.copy()
-
-        self.target_joint_positions = (
-                # self.joint_nominal_position +
-                 self.scaling_factor * action
-        )
-        self.target_joint_positions *= self.train_sim_flip
-
-        for idx, target in enumerate(self.target_joint_positions):
-            r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=80, kd=4.67
-            )
-
-        self.previous_action = action.copy()
-
-        self.sync()  # run simulation step
-        self.step_counter += 1
-
-        if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
-            self.debug_joint_status()
-
-        current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
-
-        if self.step_counter % 10 == 0:
-            self.previous_pos = current_pos.copy()
-
-        # Compute reward based on movement from previous step
-        reward = self.compute_reward(self.previous_pos, current_pos, action)
-
-        self.last_action_for_reward = action.copy()
-
-        # Fall detection and penalty
-        is_fallen = self.Player.world.global_position[2] < 0.55
-
-        # terminal state: the robot is falling or timeout
-        terminated = is_fallen or self.step_counter > 800 or self.route_completed
-        truncated = False
-
-        return self.observe(), reward, terminated, truncated, {}
-
-
-class Train(Train_Base):
-    def __init__(self, script) -> None:
-        super().__init__(script)
-
-    def train(self, args):
-
-        # --------------------------------------- Learning parameters
-        n_envs = int(os.environ.get("GYM_CPU_N_ENVS", "20"))
-        if n_envs < 1:
-            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
-        server_warmup_sec = float(os.environ.get("GYM_CPU_SERVER_WARMUP_SEC", "3.0"))
-        n_steps_per_env = int(os.environ.get("GYM_CPU_TRAIN_STEPS_PER_ENV", "512"))  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = int(os.environ.get("GYM_CPU_TRAIN_BATCH_SIZE", "512"))  # should be a factor of (n_steps_per_env * n_envs)
-        total_steps = 30000000
-        learning_rate = float(os.environ.get("GYM_CPU_TRAIN_LR", "3e-4"))
-        folder_name = f'Turn_R{self.robot_type}'
-        model_path = f'./scripts/gyms/logs/{folder_name}/'
-
-        print(f"Model path: {model_path}")
-        print(f"Using {n_envs} parallel environments")
-
-        # --------------------------------------- Run algorithm
-        def init_env(i_env, monitor=False):
-            def thunk():
-                env = WalkEnv(self.ip, self.server_p + i_env)
-                if monitor:
-                    env = Monitor(env)
-                return env
-
-            return thunk
-
-        server_log_dir = os.path.join(model_path, "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1, no_render=True, no_realtime=True)  # include 1 extra server for testing
-
-        # Wait for servers to start
-        print(f"Starting {n_envs + 1} rcssservermj servers...")
-        if server_warmup_sec > 0:
-            print(f"Waiting {server_warmup_sec:.1f}s for server warmup...")
-            sleep(server_warmup_sec)
-        print("Servers started, creating environments...")
-
-        env = SubprocVecEnv([init_env(i, monitor=True) for i in range(n_envs)], start_method="spawn")
-        # Use single-process eval env to avoid extra subprocess fragility during callback evaluation.
-        eval_env = DummyVecEnv([init_env(n_envs, monitor=True)])
-
-        try:
-            # Custom policy network architecture
-            policy_kwargs = dict(
-                net_arch=dict(
-                    pi=[512, 256, 128],  # Policy network: 3 layers
-                    vf=[512, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
-            )
-
-            if "model_file" in args:  # retrain
-                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
-                                 batch_size=minibatch_size, learning_rate=learning_rate)
-            else:  # train new model
-                model = PPO(
-                    "MlpPolicy",
-                    env=env,
-                    verbose=1,
-                    n_steps=n_steps_per_env,
-                    batch_size=minibatch_size,
-                    learning_rate=learning_rate,
-                    device="cpu",
-                    policy_kwargs=policy_kwargs,
-                    ent_coef=float(os.environ.get("GYM_CPU_TRAIN_ENT_COEF", "0.05")),  # Entropy coefficient for exploration
-                    clip_range=float(os.environ.get("GYM_CPU_TRAIN_CLIP_RANGE", "0.2")),  # PPO clipping parameter
-                    gae_lambda=0.95,  # GAE lambda
-                    gamma=float(os.environ.get("GYM_CPU_TRAIN_GAMMA", "0.95")), # Discount factor
-                    # target_kl=0.03,
-                    n_epochs=int(os.environ.get("GYM_CPU_TRAIN_EPOCHS", "5")),
-                    tensorboard_log=f"./scripts/gyms/logs/{folder_name}/tensorboard/"
-                )
-
-            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
-                                          eval_freq=n_steps_per_env * 20, save_freq=n_steps_per_env * 20, eval_eps=7,
-                                          backup_env_file=__file__)
-        except KeyboardInterrupt:
-            sleep(1)  # wait for child processes
-            print("\nctrl+c pressed, aborting...\n")
-            servers.kill()
-            return
-
-        env.close()
-        eval_env.close()
-        servers.kill()
-
-    def test(self, args):
-
-        # Uses different server and monitor ports
-        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        test_no_render = os.environ.get("GYM_CPU_TEST_NO_RENDER", "0") == "1"
-        test_no_realtime = os.environ.get("GYM_CPU_TEST_NO_REALTIME", "0") == "1"
-
-        server = Train_Server(
-            self.server_p - 1,
-            self.monitor_p,
-            1,
-            no_render=test_no_render,
-            no_realtime=test_no_realtime,
-        )
-        env = WalkEnv(self.ip, self.server_p - 1)
-        model = PPO.load(args["model_file"], env=env)
-
-        try:
-            self.export_model(args["model_file"], args["model_file"] + ".pkl",
-                              False)  # Export to pkl to create custom behavior
-            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
-        except KeyboardInterrupt:
-            print()
-
-        env.close()
-        server.kill()
-
-
-if __name__ == "__main__":
-    from types import SimpleNamespace
-
-    # 创建默认参数
-    script_args = SimpleNamespace(
-        args=SimpleNamespace(
-            i='127.0.0.1',  # Server IP
-            p=3100,  # Server port
-            m=3200,  # Monitor port
-            r=0,  # Robot type
-            t='Gym',  # Team name
-            u=1  # Uniform number
-        )
-    )
-
-    trainer = Train(script_args)
-
-    run_mode = os.environ.get("GYM_CPU_MODE", "train").strip().lower()
-
-    if run_mode == "test":
-        test_model_file = os.environ.get("GYM_CPU_TEST_MODEL", "scripts/gyms/logs/Turn_R0_004/best_model.zip")
-        test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Turn_R0_004/")
-        trainer.test({"model_file": test_model_file, "folder_dir": test_folder})
-    else:
-        retrain_model = os.environ.get("GYM_CPU_TRAIN_MODEL", "").strip()
-        if retrain_model:
-            trainer.train({"model_file": retrain_model})
-        else:
-            trainer.train({})

scripts/gyms/logs/Turn_around_normal_60deg/Walk.py

@@ -1,853 +0,0 @@
-import os
-import numpy as np
-import math
-import time
-from time import sleep
-from random import random
-from random import uniform
-from itertools import count
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.monitor import Monitor
-from stable_baselines3.common.vec_env import SubprocVecEnv, DummyVecEnv
-
-import gymnasium as gym
-from gymnasium import spaces
-
-from scripts.commons.Train_Base import Train_Base
-from scripts.commons.Server import Server as Train_Server
-
-from agent.base_agent import Base_Agent
-from utils.math_ops import MathOps
-
-from scipy.spatial.transform import Rotation as R
-
-'''
-Objective:
-Learn how to run forward using step primitive
-----------
-- class Basic_Run: implements an OpenAI custom gym
-- class Train:  implements algorithms to train a new model or test an existing model
-'''
-
-
-class WalkEnv(gym.Env):
-    def __init__(self, ip, server_p) -> None:
-
-        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
-        self.Player = player = Base_Agent(
-            team_name="Gym",
-            number=1,
-            host=ip,
-            port=server_p
-        )
-        self.robot_type = self.Player.robot
-        self.step_counter = 0  # to limit episode size
-        self.force_play_on = True
-
-        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
-        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
-        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
-        self.isfallen = False
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.debug_every_n_steps = 5
-        self.enable_debug_joint_status = False
-        self.reward_debug_interval_sec = float(os.environ.get("GYM_CPU_REWARD_DEBUG_INTERVAL_SEC", "600"))
-        self.reward_debug_burst_steps = int(os.environ.get("GYM_CPU_REWARD_DEBUG_BURST_STEPS", "10"))
-        self._reward_debug_last_time = time.time()
-        self._reward_debug_steps_left = 0
-        self.calibrate_nominal_from_neutral = True
-        self.auto_calibrate_train_sim_flip = True
-        self.nominal_calibrated_once = False
-        self.flip_calibrated_once = False
-        self._target_hz = 0.0
-        self._target_dt = 0.0
-        self._last_sync_time = None
-        target_hz_env = 0
-        if target_hz_env:
-            try:
-                self._target_hz = float(target_hz_env)
-            except ValueError:
-                self._target_hz = 0.0
-        if self._target_hz > 0.0:
-            self._target_dt = 1.0 / self._target_hz
-
-        # State space
-        # 原始观测大小: 78
-        obs_size = 78
-        self.obs = np.zeros(obs_size, np.float32)
-        self.observation_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(obs_size,),
-            dtype=np.float32
-        )
-
-        action_dim = len(self.Player.robot.ROBOT_MOTORS)
-        self.no_of_actions = action_dim
-        self.action_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(action_dim,),
-            dtype=np.float32
-        )
-
-        # 中立姿态
-        self.joint_nominal_position = np.array(
-            [
-                0.0,  # 0: Head_yaw (he1)
-                0.0,  # 1: Head_pitch (he2)
-                0.0,  # 2: Left_Shoulder_Pitch (lae1)
-                0.0,  # 3: Left_Shoulder_Roll (lae2)
-                0.0,  # 4: Left_Elbow_Pitch (lae3)
-                0.0,  # 5: Left_Elbow_Yaw (lae4)
-                0.0,  # 6: Right_Shoulder_Pitch (rae1)
-                0.0,  # 7: Right_Shoulder_Roll (rae2)
-                0.0,  # 8: Right_Elbow_Pitch (rae3)
-                0.0,  # 9: Right_Elbow_Yaw (rae4)
-                0.0,  # 10: Waist (te1)
-                0.0,  # 11: Left_Hip_Pitch (lle1)
-                0.0,  # 12: Left_Hip_Roll (lle2)
-                1.0,  # 13: Left_Hip_Yaw (lle3)
-                0.0,  # 14: Left_Knee_Pitch (lle4)
-                0.0,  # 15: Left_Ankle_Pitch (lle5)
-                0.0,  # 16: Left_Ankle_Roll (lle6)
-                0.0,  # 17: Right_Hip_Pitch (rle1)
-                0.0,  # 18: Right_Hip_Roll (rle2)
-                1.0,  # 19: Right_Hip_Yaw (rle3)
-                0.0,  # 20: Right_Knee_Pitch (rle4)
-                0.0,  # 21: Right_Ankle_Pitch (rle5)
-                0.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-        self.joint_nominal_position = np.zeros(self.no_of_actions)
-        self.train_sim_flip = np.array(
-            [
-                1.0,  # 0: Head_yaw (he1)
-                -1.0,  # 1: Head_pitch (he2)
-                1.0,  # 2: Left_Shoulder_Pitch (lae1)
-                -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                -1.0,  # 4: Left_Elbow_Pitch (lae3)
-                1.0,  # 5: Left_Elbow_Yaw (lae4)
-                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                -1.0,  # 7: Right_Shoulder_Roll (rae2)
-                1.0,  # 8: Right_Elbow_Pitch (rae3)
-                1.0,  # 9: Right_Elbow_Yaw (rae4)
-                1.0,  # 10: Waist (te1)
-                1.0,  # 11: Left_Hip_Pitch (lle1)
-                -1.0,  # 12: Left_Hip_Roll (lle2)
-                -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,  # 14: Left_Knee_Pitch (lle4)
-                1.0,  # 15: Left_Ankle_Pitch (lle5)
-                -1.0,  # 16: Left_Ankle_Roll (lle6)
-                -1.0,  # 17: Right_Hip_Pitch (rle1)
-                -1.0,  # 18: Right_Hip_Roll (rle2)
-                -1.0,  # 19: Right_Hip_Yaw (rle3)
-                -1.0,  # 20: Right_Knee_Pitch (rle4)
-                -1.0,  # 21: Right_Ankle_Pitch (rle5)
-                -1.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-
-        self.scaling_factor = 0.3
-        # self.scaling_factor = 1
-
-        # Encourage a minimum lateral stance so the policy avoids feet overlap.
-        self.min_stance_rad = 0.10
-
-        # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = False
-        self.reset_beam_yaw_range_deg = float(os.environ.get("GYM_CPU_RESET_BEAM_YAW_RANGE_DEG", "180"))
-        self.reset_target_bearing_range_deg = float(os.environ.get("GYM_CPU_RESET_TARGET_BEARING_RANGE_DEG", "90"))
-        self.reset_target_distance_min = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MIN", "1.2"))
-        self.reset_target_distance_max = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MAX", "2.8"))
-        if self.reset_target_distance_min > self.reset_target_distance_max:
-            self.reset_target_distance_min, self.reset_target_distance_max = (
-                self.reset_target_distance_max,
-                self.reset_target_distance_min,
-            )
-        self.reset_joint_noise_rad = 0.025
-        self.reset_perturb_steps = 4
-        self.reset_recover_steps = 8
-
-        self.reward_smoothness_scale = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_SCALE", "0.06"))
-        self.reward_smoothness_cap = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_CAP", "0.45"))
-        self.reward_head_toward_bonus = float(os.environ.get("GYM_CPU_REWARD_HEAD_TOWARD_BONUS", "1"))
-
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Track previous position
-        self.last_yaw_error = None
-        self.Player.server.connect()
-        # sleep(2.0)  # Longer wait for connection to establish completely
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-        self.start_time = time.time()
-
-    def _reconnect_server(self):
-        try:
-            self.Player.server.shutdown()
-        except Exception:
-            pass
-
-        self.Player.server.connect()
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-
-    def _safe_receive_world_update(self, retries=1):
-        last_exc = None
-        for attempt in range(retries + 1):
-            try:
-                self.Player.server.receive()
-                self.Player.world.update()
-                return
-            except (ConnectionResetError, OSError) as exc:
-                last_exc = exc
-                if attempt >= retries:
-                    raise
-                self._reconnect_server()
-        if last_exc is not None:
-            raise last_exc
-
-    def debug_log(self, message):
-        print(message)
-        try:
-            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
-            with open(log_path, "a", encoding="utf-8") as f:
-                f.write(message + "\n")
-        except OSError:
-            pass
-
-    @staticmethod
-    def _wrap_to_pi(angle_rad: float) -> float:
-        return (angle_rad + math.pi) % (2.0 * math.pi) - math.pi
-
-    def observe(self, init=False):
-
-        """获取当前观测值"""
-        robot = self.Player.robot
-        world = self.Player.world
-
-        # Safety check: ensure data is available
-
-        # 计算目标速度
-        raw_target = self.target_position - world.global_position[:2]
-        velocity = MathOps.rotate_2d_vec(
-            raw_target,
-            -robot.global_orientation_euler[2],
-            is_rad=False
-        )
-
-        # 计算相对方向
-        rel_orientation = MathOps.vector_angle(velocity) * 0.3
-        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
-
-        velocity = np.concatenate([velocity, np.array([rel_orientation])])
-        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
-        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
-
-        # 关节状态
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        qpos_qvel_previous_action = np.concatenate([
-            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
-            radian_joint_speeds / 110.0 * self.train_sim_flip,
-            self.previous_action / 10.0,
-        ])
-
-        # 角速度
-        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
-
-        # 投影的重力方向
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-
-        # 组合观测
-        observation = np.concatenate([
-            qpos_qvel_previous_action,
-            ang_vel,
-            velocity,
-            projected_gravity,
-        ])
-
-        observation = np.clip(observation, -10.0, 10.0)
-        return observation.astype(np.float32)
-
-    def sync(self):
-        ''' Run a single simulation step '''
-        self._safe_receive_world_update(retries=1)
-        self.Player.robot.commit_motor_targets_pd()
-        self.Player.server.send()
-        if self._target_dt > 0.0:
-            now = time.time()
-            if self._last_sync_time is None:
-                self._last_sync_time = now
-                return
-            elapsed = now - self._last_sync_time
-            remaining = self._target_dt - elapsed
-            if remaining > 0.0:
-                time.sleep(remaining)
-                now = time.time()
-            self._last_sync_time = now
-
-    def debug_joint_status(self):
-        robot = self.Player.robot
-        actual_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        target_joint_positions = getattr(
-            self,
-            'target_joint_positions',
-            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
-        )
-        joint_error = actual_joint_positions - target_joint_positions
-        leg_slice = slice(11, None)
-
-        self.debug_log(
-            "[WalkDebug] "
-            f"step={self.step_counter} "
-            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
-            f"target_xy={np.round(self.target_position, 3).tolist()} "
-            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
-            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
-            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
-            f"fallen={self.Player.world.global_position[2] < 0.3}"
-        )
-        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
-
-    def reset(self, seed=None, options=None):
-        '''
-        Reset and stabilize the robot
-        Note: for some behaviors it would be better to reduce stabilization or add noise
-        '''
-        r = self.Player.robot
-        super().reset(seed=seed)
-        if seed is not None:
-            np.random.seed(seed)
-
-        target_distance = np.random.uniform(self.reset_target_distance_min, self.reset_target_distance_max)
-        target_bearing_deg = np.random.uniform(-self.reset_target_bearing_range_deg, self.reset_target_bearing_range_deg)
-
-        self.step_counter = 0
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
-        self.last_yaw_error = None
-        self.walk_cycle_step = 0
-        self._reward_debug_steps_left = 0
-
-        # 随机 beam 目标位置和朝向，增加训练多样性
-        beam_x = (random() - 0.5) * 10
-        beam_y = (random() - 0.5) * 10
-        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
-
-        for _ in range(5):
-            self._safe_receive_world_update(retries=2)
-            self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
-            self.Player.server.send()
-
-        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
-        finished_count = 0
-        for _ in range(50):
-            finished = self.Player.skills_manager.execute("Neutral")
-            self.sync()
-            if finished:
-                finished_count += 1
-                if finished_count >= 20:  # 假设需要连续20次完成才算成功
-                    break
-
-        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-            # Perturb waist + lower body only (10:), keep head/arms stable.
-            perturb_action[10:] = np.random.uniform(
-                -self.reset_joint_noise_rad,
-                self.reset_joint_noise_rad,
-                size=(self.no_of_actions - 10,)
-            )
-
-            for _ in range(self.reset_perturb_steps):
-                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-            for i in range(self.reset_recover_steps):
-                # Linearly fade perturbation to help policy start from near-neutral.
-                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-        # memory variables
-        self.sync()  
-        self.initial_position = np.array(self.Player.world.global_position[:2])
-        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions, np.float32)
-        # Randomize global target bearing so policy must learn to rotate toward it first.
-        heading_deg = float(r.global_orientation_euler[2])
-        target_offset = MathOps.rotate_2d_vec(
-            np.array([target_distance, 0.0]),
-            heading_deg + target_bearing_deg,
-            is_rad=False,
-        )
-        point1 = self.initial_position + target_offset
-        self.point_list = [point1]
-        self.target_position = self.point_list[self.waypoint_index]
-        self.initial_height = self.Player.world.global_position[2]
-
-        return self.observe(True), {}
-
-    def render(self, mode='human', close=False):
-        return
-
-    
-    def compute_reward(self, previous_pos, current_pos, action):
-        height = float(self.Player.world.global_position[2])
-        robot = self.Player.robot
-
-
-        joint_pos_rad = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        joint_speed_rad = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-        tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        ang_vel = np.deg2rad(robot.gyroscope)
-        rp_ang_vel_mag = float(np.linalg.norm(ang_vel[:2]))
-
-        is_fallen = height < 0.55
-        if is_fallen:
-            # remain = max(0, 800 - self.step_counter)
-            # return -8.0 - 0.01 * remain
-            return -20.0
-        
-
-        if np.linalg.norm(current_pos - previous_pos) > 0.005:
-            position_penalty = -3 * float(np.linalg.norm(current_pos - previous_pos))
-        else:
-            position_penalty = 0.0
-
-
-        # Turn-to-target shaping.
-        to_target = self.target_position - current_pos
-        dist_to_target = float(np.linalg.norm(to_target))
-        if dist_to_target > 1e-6:
-            target_yaw = math.atan2(float(to_target[1]), float(to_target[0]))
-        else:
-            target_yaw = 0.0
-
-        robot_yaw = math.radians(float(robot.global_orientation_euler[2]))
-        yaw_error = target_yaw - robot_yaw
-
-        # Main heading objective: face the target direction.
-        # heading_align_reward = 1.0 * math.cos(yaw_error)
-
-        abs_yaw_error = abs(yaw_error)
-        alive_bonus = 2.0 * max(0.0, 1.0 - abs_yaw_error / math.pi)
-        head_toward_bonus = self.reward_head_toward_bonus if abs_yaw_error < math.radians(4.0) else 0.0
-
-        if self.last_yaw_error is None:
-            heading_progress_reward = 0.0
-        else:
-            prev_abs_yaw_error = abs(self.last_yaw_error)
-            yaw_err_delta = prev_abs_yaw_error - abs_yaw_error
-            progress_gate = 1.0 if abs_yaw_error > math.radians(4.0) else 0.0
-            heading_progress_reward =  0.8 * progress_gate * yaw_err_delta
-            heading_progress_reward = float(np.clip(heading_progress_reward, -0.4, 0.4))
-        self.last_yaw_error = yaw_error
-
-        # action_penalty = -0.01 * float(np.linalg.norm(action))
-        smoothness_penalty = -0.05 * float(np.linalg.norm(action - self.last_action_for_reward))
-
-        posture_penalty = -0.6 * tilt_mag
-        # Penalize roll/pitch rotational shake but do not penalize yaw turning directly.
-        ang_vel_penalty = -0.06 * rp_ang_vel_mag
-
-        joint_pos = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        ) * self.train_sim_flip
-        left_hip_roll = float(joint_pos[12])
-        right_hip_roll = float(joint_pos[18])
-        left_hip_pitch = float(joint_pos[11])
-        right_hip_pitch = float(joint_pos[17])
-
-        left_ankle_roll = float(joint_pos[16])
-        right_ankle_roll = float(joint_pos[22])
-
-        max_leg_roll = 0.2  # 防止劈叉姿势
-        split_penalty = -0.8 * max(0.0, (-left_hip_roll + right_hip_roll - 2 * max_leg_roll) / max_leg_roll)
-        left_hip_yaw = float(joint_pos[13])
-        right_hip_yaw = float(joint_pos[19])
-
-        min_leg_separation = 0.05  # 最小腿间距（防止贴得太近）
-        # 惩罚腿过分靠拢（内收）- 基于两腿间距
-        leg_separation = -left_hip_roll + right_hip_roll
-        inward_penalty = -0.25 * max(0.0, (min_leg_separation - leg_separation) / min_leg_separation)
-        
-        
-        # 脚踝roll角度检测：防止过度外翻或内翻
-        max_ankle_roll = 0.15  # 最大允许的脚踝roll角度
-        
-        # 惩罚脚踝过度外翻/内翻（绝对值过大）
-        ankle_roll_penalty = -0.5 * max(0.0, (abs(left_ankle_roll) + abs(right_ankle_roll) - 2 * max_ankle_roll) / max_ankle_roll)
-        
-        # 惩罚两脚踝roll方向相反（不稳定姿势）
-        ankle_roll_cross_penalty = -0.3 * max(0.0, -(left_ankle_roll * right_ankle_roll))
-
-       # 分别惩罚左右大腿过度转动
-        max_hip_yaw = 0.4  # 最大允许的yaw角度
-        left_hip_yaw_penalty = -0.4 * max(0.0, abs(left_hip_yaw) - max_hip_yaw)
-        right_hip_yaw_penalty = -0.4 * max(0.0, abs(right_hip_yaw) - max_hip_yaw)
-        # 智能交叉腿惩罚：只在站立时惩罚，转身时允许交叉腿
-        yaw_rate = float(np.deg2rad(robot.gyroscope[2]))
-        yaw_rate_abs = abs(yaw_rate)
-
-        # 当转身速度较小时才惩罚交叉腿（站立状态）
-        cross_leg_gate = max(0.0, 1.0 - yaw_rate_abs / math.radians(8.0))
-        hip_yaw_cross_penalty = -1.0 * cross_leg_gate * max(0.0, -(left_hip_yaw * right_hip_yaw)) if left_hip_yaw > 0 and right_hip_yaw < 0 else 0.0
-
-        # Torso-lower-body linkage: reward coordinated turning, punish waist-only spinning.
-        waist_speed = abs(float(joint_speed_rad[10]))
-        lower_body_speed = float(np.mean(np.abs(joint_speed_rad[11:23])))
-        lower_body_follow_ratio = lower_body_speed / (waist_speed + 1e-4)
-        linkage_reward = 0.24 * min(1.0, lower_body_follow_ratio) * min(1.0, waist_speed / 1.2)
-        waist_only_turn_penalty = -0.20 * max(0.0, waist_speed - 1.35 * lower_body_speed)
-
-        # Extra posture linkage in yaw joints to avoid decoupled torso twist.
-        waist_yaw = abs(float(joint_pos_rad[10]))
-        hip_yaw_mean = 0.5 * (abs(float(joint_pos_rad[13])) + abs(float(joint_pos_rad[19])))
-        yaw_link_reward = 0.12 * math.exp(-abs(waist_yaw - hip_yaw_mean) / 0.22)
-
-        target_height = self.initial_height
-        height_error =  height - target_height
-        height_error = height - target_height
-        
-        height_penalty = -(math.exp(12*abs(height_error))-1) if height_error > 0.04 else 0
-
-        # # 在 compute_reward 开头附近，添加高度变化率计算
-        # if not hasattr(self, 'last_height'):
-        #     self.last_height = height
-        #     self.last_height_time = self.step_counter  # 可选，用于时间间隔
-        # height_rate = height - self.last_height  # 正为上升，负为下降
-        # self.last_height = height
-
-        # 惩罚高度下降（负变化率）
-        # height_down_penalty = -5.0 * max(0, -height_rate)  # 系数可调，-height_rate 为正表示下降幅度
-
-        # # 在 compute_reward 中
-        # if self.step_counter > 50:
-        #     avg_prev_action = np.mean(self.prev_action_history, axis=0)
-        #     novelty = float(np.linalg.norm(action - avg_prev_action))
-        #     exploration_bonus = 0.05 * novelty
-        # else:
-        #     exploration_bonus = 0
-
-        # self.prev_action_history[self.history_idx] = action
-        # self.history_idx = (self.history_idx + 1) % 50
-
-
-        total = (
-            # progress_reward  + 
-                alive_bonus + 
-                head_toward_bonus +
-                heading_progress_reward +
-                # lateral_penalty +
-                # action_penalty +
-                smoothness_penalty +
-                posture_penalty
-                + ang_vel_penalty
-                + height_penalty
-                + ankle_roll_penalty
-                + ankle_roll_cross_penalty
-                + split_penalty
-                + inward_penalty
-                # + leg_proximity_penalty
-                # + left_hip_yaw_penalty
-                # + right_hip_yaw_penalty
-                # + hip_yaw_cross_penalty
-                + position_penalty
-                # + linkage_reward
-                # + waist_only_turn_penalty
-                # + yaw_link_reward
-                # + stance_collapse_penalty
-                # + hip_yaw_yaw_cross_penalty
-                # + stance_collapse_penalty
-                #  + cross_leg_penalty
-                #  + exploration_bonus
-                # + height_down_penalty
-                 )
-        # print(height_error, height_penalty)
-        
-        now = time.time()
-        if self.reward_debug_interval_sec > 0 and now - self._reward_debug_last_time >= self.reward_debug_interval_sec:
-            self._reward_debug_last_time = now
-            self._reward_debug_steps_left = max(1, self.reward_debug_burst_steps)
-
-        if self._reward_debug_steps_left > 0:
-            self._reward_debug_steps_left -= 1
-            self.debug_log(
-                f"height_penalty:{height_penalty:.4f},"
-                f"smoothness_penalty:{smoothness_penalty:.4f},"
-                f"posture_penalty:{posture_penalty:.4f},"
-                f"heading_progress_reward:{heading_progress_reward:.4f},"
-                # f"stance_collapse_penalty:{stance_collapse_penalty:.4f},"
-                # f"cross_leg_penalty:{cross_leg_penalty:.4f},"
-                f"ang_vel_penalty:{ang_vel_penalty:.4f},"
-                f"split_penalty:{split_penalty:.4f},"
-                f"ankle_roll_penalty:{ankle_roll_penalty:.4f},"
-                f"ankle_roll_cross_penalty:{ankle_roll_cross_penalty:.4f},"
-                f"left_hip_yaw_penalty:{left_hip_yaw_penalty:.4f},"
-                f"right_hip_yaw_penalty:{right_hip_yaw_penalty:.4f},"
-                f"hip_yaw_cross_penalty:{hip_yaw_cross_penalty:.4f},"
-                f"inward_penalty:{inward_penalty:.4f},"
-                f"position_penalty:{position_penalty:.4f},"
-                # f"linkage_reward:{linkage_reward:.4f},"
-                # f"waist_only_turn_penalty:{waist_only_turn_penalty:.4f},"
-                # f"yaw_link_reward:{yaw_link_reward:.4f}"
-                # f"leg_proximity_penalty:{leg_proximity_penalty:.4f},"
-
-                # f"stance_collapse_penalty:{stance_collapse_penalty:.4f},"
-                # f"hip_yaw_yaw_cross_penalty:{hip_yaw_yaw_cross_penalty:.4f},"
-                #   f"height_down_penalty:{height_down_penalty:.4f}",
-                #   f"exploration_bonus:{exploration_bonus:.4f}"
-                f"alive_bonus:{alive_bonus:.4f},"
-                f"abs_yaw_error:{abs_yaw_error:.4f}"
-                f"total:{total:.4f}"
-                )
-        # print(f"abs_yaw_error:{abs_yaw_error:.4f}")
-        return total
-
-
-
-    def step(self, action):
-
-        r = self.Player.robot
-        max_action_delta =  0.5# Limit how much the action can change from the previous step to encourage smoother motions.
-        if self.previous_action is not None:
-            action = np.clip(action, self.previous_action - max_action_delta, self.previous_action + max_action_delta)
-        action[0:2] = 0
-        action[3] = 4
-        action[7] = -4
-        action[2] = 0
-        action[6] = 0
-        action[4] = 0
-        action[5] = -5
-        action[8] = 0
-        action[9] = 5
-        action[10] = 0
-        action[11] = np.clip(action[11], -0.4, 0.4)
-        action[17] = np.clip(action[17], -0.4, 0.4)
-        # action[12] = -1.0
-        # action[18] = 1.0
-        # action[13] = -1.0
-        # action[19] = 1.0
-        self.previous_action = action.copy()
-
-        self.target_joint_positions = (
-                # self.joint_nominal_position +
-                 self.scaling_factor * action
-        )
-        self.target_joint_positions *= self.train_sim_flip
-
-        for idx, target in enumerate(self.target_joint_positions):
-            r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=80, kd=4.67
-            )
-
-        self.previous_action = action.copy()
-
-        self.sync()  # run simulation step
-        self.step_counter += 1
-
-        if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
-            self.debug_joint_status()
-
-        current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
-
-        if self.step_counter % 10 == 0:
-            self.previous_pos = current_pos.copy()
-
-        # Compute reward based on movement from previous step
-        reward = self.compute_reward(self.previous_pos, current_pos, action)
-
-        self.last_action_for_reward = action.copy()
-
-        # Fall detection and penalty
-        is_fallen = self.Player.world.global_position[2] < 0.55
-
-        # terminal state: the robot is falling or timeout
-        terminated = is_fallen or self.step_counter > 800 or self.route_completed
-        truncated = False
-
-        return self.observe(), reward, terminated, truncated, {}
-
-
-class Train(Train_Base):
-    def __init__(self, script) -> None:
-        super().__init__(script)
-
-    def train(self, args):
-
-        # --------------------------------------- Learning parameters
-        n_envs = int(os.environ.get("GYM_CPU_N_ENVS", "20"))
-        if n_envs < 1:
-            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
-        server_warmup_sec = float(os.environ.get("GYM_CPU_SERVER_WARMUP_SEC", "3.0"))
-        n_steps_per_env = int(os.environ.get("GYM_CPU_TRAIN_STEPS_PER_ENV", "512"))  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = int(os.environ.get("GYM_CPU_TRAIN_BATCH_SIZE", "512"))  # should be a factor of (n_steps_per_env * n_envs)
-        total_steps = 30000000
-        learning_rate = float(os.environ.get("GYM_CPU_TRAIN_LR", "3e-4"))
-        folder_name = f'Turn_R{self.robot_type}'
-        model_path = f'./scripts/gyms/logs/{folder_name}/'
-
-        print(f"Model path: {model_path}")
-        print(f"Using {n_envs} parallel environments")
-
-        # --------------------------------------- Run algorithm
-        def init_env(i_env, monitor=False):
-            def thunk():
-                env = WalkEnv(self.ip, self.server_p + i_env)
-                if monitor:
-                    env = Monitor(env)
-                return env
-
-            return thunk
-
-        server_log_dir = os.path.join(model_path, "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1, no_render=True, no_realtime=True)  # include 1 extra server for testing
-
-        # Wait for servers to start
-        print(f"Starting {n_envs + 1} rcssservermj servers...")
-        if server_warmup_sec > 0:
-            print(f"Waiting {server_warmup_sec:.1f}s for server warmup...")
-            sleep(server_warmup_sec)
-        print("Servers started, creating environments...")
-
-        env = SubprocVecEnv([init_env(i, monitor=True) for i in range(n_envs)], start_method="spawn")
-        # Use single-process eval env to avoid extra subprocess fragility during callback evaluation.
-        eval_env = DummyVecEnv([init_env(n_envs, monitor=True)])
-
-        try:
-            # Custom policy network architecture
-            policy_kwargs = dict(
-                net_arch=dict(
-                    pi=[512, 256, 128],  # Policy network: 3 layers
-                    vf=[512, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
-            )
-
-            if "model_file" in args:  # retrain
-                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
-                                 batch_size=minibatch_size, learning_rate=learning_rate)
-            else:  # train new model
-                model = PPO(
-                    "MlpPolicy",
-                    env=env,
-                    verbose=1,
-                    n_steps=n_steps_per_env,
-                    batch_size=minibatch_size,
-                    learning_rate=learning_rate,
-                    device="cpu",
-                    policy_kwargs=policy_kwargs,
-                    ent_coef=float(os.environ.get("GYM_CPU_TRAIN_ENT_COEF", "0.05")),  # Entropy coefficient for exploration
-                    clip_range=float(os.environ.get("GYM_CPU_TRAIN_CLIP_RANGE", "0.2")),  # PPO clipping parameter
-                    gae_lambda=0.95,  # GAE lambda
-                    gamma=float(os.environ.get("GYM_CPU_TRAIN_GAMMA", "0.95")), # Discount factor
-                    # target_kl=0.03,
-                    n_epochs=int(os.environ.get("GYM_CPU_TRAIN_EPOCHS", "5")),
-                    tensorboard_log=f"./scripts/gyms/logs/{folder_name}/tensorboard/"
-                )
-
-            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
-                                          eval_freq=n_steps_per_env * 20, save_freq=n_steps_per_env * 20, eval_eps=7,
-                                          backup_env_file=__file__)
-        except KeyboardInterrupt:
-            sleep(1)  # wait for child processes
-            print("\nctrl+c pressed, aborting...\n")
-            servers.kill()
-            return
-
-        env.close()
-        eval_env.close()
-        servers.kill()
-
-    def test(self, args):
-
-        # Uses different server and monitor ports
-        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        test_no_render = os.environ.get("GYM_CPU_TEST_NO_RENDER", "0") == "1"
-        test_no_realtime = os.environ.get("GYM_CPU_TEST_NO_REALTIME", "0") == "1"
-
-        server = Train_Server(
-            self.server_p - 1,
-            self.monitor_p,
-            1,
-            no_render=test_no_render,
-            no_realtime=test_no_realtime,
-        )
-        env = WalkEnv(self.ip, self.server_p - 1)
-        model = PPO.load(args["model_file"], env=env)
-
-        try:
-            self.export_model(args["model_file"], args["model_file"] + ".pkl",
-                              False)  # Export to pkl to create custom behavior
-            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
-        except KeyboardInterrupt:
-            print()
-
-        env.close()
-        server.kill()
-
-
-if __name__ == "__main__":
-    from types import SimpleNamespace
-
-    # 创建默认参数
-    script_args = SimpleNamespace(
-        args=SimpleNamespace(
-            i='127.0.0.1',  # Server IP
-            p=3100,  # Server port
-            m=3200,  # Monitor port
-            r=0,  # Robot type
-            t='Gym',  # Team name
-            u=1  # Uniform number
-        )
-    )
-
-    trainer = Train(script_args)
-
-    run_mode = os.environ.get("GYM_CPU_MODE", "train").strip().lower()
-
-    if run_mode == "test":
-        test_model_file = os.environ.get("GYM_CPU_TEST_MODEL", "scripts/gyms/logs/Turn_R0_004/best_model.zip")
-        test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Turn_R0_004/")
-        trainer.test({"model_file": test_model_file, "folder_dir": test_folder})
-    else:
-        retrain_model = os.environ.get("GYM_CPU_TRAIN_MODEL", "").strip()
-        if retrain_model:
-            trainer.train({"model_file": retrain_model})
-        else:
-            trainer.train({})

scripts/gyms/logs/Turn_around_unnormal/Walk.py

@@ -1,853 +0,0 @@
-import os
-import numpy as np
-import math
-import time
-from time import sleep
-from random import random
-from random import uniform
-from itertools import count
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.monitor import Monitor
-from stable_baselines3.common.vec_env import SubprocVecEnv, DummyVecEnv
-
-import gymnasium as gym
-from gymnasium import spaces
-
-from scripts.commons.Train_Base import Train_Base
-from scripts.commons.Server import Server as Train_Server
-
-from agent.base_agent import Base_Agent
-from utils.math_ops import MathOps
-
-from scipy.spatial.transform import Rotation as R
-
-'''
-Objective:
-Learn how to run forward using step primitive
-----------
-- class Basic_Run: implements an OpenAI custom gym
-- class Train:  implements algorithms to train a new model or test an existing model
-'''
-
-
-class WalkEnv(gym.Env):
-    def __init__(self, ip, server_p) -> None:
-
-        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
-        self.Player = player = Base_Agent(
-            team_name="Gym",
-            number=1,
-            host=ip,
-            port=server_p
-        )
-        self.robot_type = self.Player.robot
-        self.step_counter = 0  # to limit episode size
-        self.force_play_on = True
-
-        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
-        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
-        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
-        self.isfallen = False
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.debug_every_n_steps = 5
-        self.enable_debug_joint_status = False
-        self.reward_debug_interval_sec = float(os.environ.get("GYM_CPU_REWARD_DEBUG_INTERVAL_SEC", "600"))
-        self.reward_debug_burst_steps = int(os.environ.get("GYM_CPU_REWARD_DEBUG_BURST_STEPS", "10"))
-        self._reward_debug_last_time = time.time()
-        self._reward_debug_steps_left = 0
-        self.calibrate_nominal_from_neutral = True
-        self.auto_calibrate_train_sim_flip = True
-        self.nominal_calibrated_once = False
-        self.flip_calibrated_once = False
-        self._target_hz = 0.0
-        self._target_dt = 0.0
-        self._last_sync_time = None
-        target_hz_env = 0
-        if target_hz_env:
-            try:
-                self._target_hz = float(target_hz_env)
-            except ValueError:
-                self._target_hz = 0.0
-        if self._target_hz > 0.0:
-            self._target_dt = 1.0 / self._target_hz
-
-        # State space
-        # 原始观测大小: 78
-        obs_size = 78
-        self.obs = np.zeros(obs_size, np.float32)
-        self.observation_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(obs_size,),
-            dtype=np.float32
-        )
-
-        action_dim = len(self.Player.robot.ROBOT_MOTORS)
-        self.no_of_actions = action_dim
-        self.action_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(action_dim,),
-            dtype=np.float32
-        )
-
-        # 中立姿态
-        self.joint_nominal_position = np.array(
-            [
-                0.0,  # 0: Head_yaw (he1)
-                0.0,  # 1: Head_pitch (he2)
-                0.0,  # 2: Left_Shoulder_Pitch (lae1)
-                0.0,  # 3: Left_Shoulder_Roll (lae2)
-                0.0,  # 4: Left_Elbow_Pitch (lae3)
-                0.0,  # 5: Left_Elbow_Yaw (lae4)
-                0.0,  # 6: Right_Shoulder_Pitch (rae1)
-                0.0,  # 7: Right_Shoulder_Roll (rae2)
-                0.0,  # 8: Right_Elbow_Pitch (rae3)
-                0.0,  # 9: Right_Elbow_Yaw (rae4)
-                0.0,  # 10: Waist (te1)
-                0.0,  # 11: Left_Hip_Pitch (lle1)
-                0.0,  # 12: Left_Hip_Roll (lle2)
-                1.0,  # 13: Left_Hip_Yaw (lle3)
-                0.0,  # 14: Left_Knee_Pitch (lle4)
-                0.0,  # 15: Left_Ankle_Pitch (lle5)
-                0.0,  # 16: Left_Ankle_Roll (lle6)
-                0.0,  # 17: Right_Hip_Pitch (rle1)
-                0.0,  # 18: Right_Hip_Roll (rle2)
-                1.0,  # 19: Right_Hip_Yaw (rle3)
-                0.0,  # 20: Right_Knee_Pitch (rle4)
-                0.0,  # 21: Right_Ankle_Pitch (rle5)
-                0.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-        self.joint_nominal_position = np.zeros(self.no_of_actions)
-        self.train_sim_flip = np.array(
-            [
-                1.0,  # 0: Head_yaw (he1)
-                -1.0,  # 1: Head_pitch (he2)
-                1.0,  # 2: Left_Shoulder_Pitch (lae1)
-                -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                -1.0,  # 4: Left_Elbow_Pitch (lae3)
-                1.0,  # 5: Left_Elbow_Yaw (lae4)
-                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                -1.0,  # 7: Right_Shoulder_Roll (rae2)
-                1.0,  # 8: Right_Elbow_Pitch (rae3)
-                1.0,  # 9: Right_Elbow_Yaw (rae4)
-                1.0,  # 10: Waist (te1)
-                1.0,  # 11: Left_Hip_Pitch (lle1)
-                -1.0,  # 12: Left_Hip_Roll (lle2)
-                -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,  # 14: Left_Knee_Pitch (lle4)
-                1.0,  # 15: Left_Ankle_Pitch (lle5)
-                -1.0,  # 16: Left_Ankle_Roll (lle6)
-                -1.0,  # 17: Right_Hip_Pitch (rle1)
-                -1.0,  # 18: Right_Hip_Roll (rle2)
-                -1.0,  # 19: Right_Hip_Yaw (rle3)
-                -1.0,  # 20: Right_Knee_Pitch (rle4)
-                -1.0,  # 21: Right_Ankle_Pitch (rle5)
-                -1.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-
-        self.scaling_factor = 0.3
-        # self.scaling_factor = 1
-
-        # Encourage a minimum lateral stance so the policy avoids feet overlap.
-        self.min_stance_rad = 0.10
-
-        # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = False
-        self.reset_beam_yaw_range_deg = float(os.environ.get("GYM_CPU_RESET_BEAM_YAW_RANGE_DEG", "180"))
-        self.reset_target_bearing_range_deg = float(os.environ.get("GYM_CPU_RESET_TARGET_BEARING_RANGE_DEG", "45"))
-        self.reset_target_distance_min = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MIN", "1.2"))
-        self.reset_target_distance_max = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MAX", "2.8"))
-        if self.reset_target_distance_min > self.reset_target_distance_max:
-            self.reset_target_distance_min, self.reset_target_distance_max = (
-                self.reset_target_distance_max,
-                self.reset_target_distance_min,
-            )
-        self.reset_joint_noise_rad = 0.025
-        self.reset_perturb_steps = 4
-        self.reset_recover_steps = 8
-
-        self.reward_smoothness_scale = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_SCALE", "0.06"))
-        self.reward_smoothness_cap = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_CAP", "0.45"))
-        self.reward_head_toward_bonus = float(os.environ.get("GYM_CPU_REWARD_HEAD_TOWARD_BONUS", "1"))
-
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Track previous position
-        self.last_yaw_error = None
-        self.Player.server.connect()
-        # sleep(2.0)  # Longer wait for connection to establish completely
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-        self.start_time = time.time()
-
-    def _reconnect_server(self):
-        try:
-            self.Player.server.shutdown()
-        except Exception:
-            pass
-
-        self.Player.server.connect()
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-
-    def _safe_receive_world_update(self, retries=1):
-        last_exc = None
-        for attempt in range(retries + 1):
-            try:
-                self.Player.server.receive()
-                self.Player.world.update()
-                return
-            except (ConnectionResetError, OSError) as exc:
-                last_exc = exc
-                if attempt >= retries:
-                    raise
-                self._reconnect_server()
-        if last_exc is not None:
-            raise last_exc
-
-    def debug_log(self, message):
-        print(message)
-        try:
-            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
-            with open(log_path, "a", encoding="utf-8") as f:
-                f.write(message + "\n")
-        except OSError:
-            pass
-
-    @staticmethod
-    def _wrap_to_pi(angle_rad: float) -> float:
-        return (angle_rad + math.pi) % (2.0 * math.pi) - math.pi
-
-    def observe(self, init=False):
-
-        """获取当前观测值"""
-        robot = self.Player.robot
-        world = self.Player.world
-
-        # Safety check: ensure data is available
-
-        # 计算目标速度
-        raw_target = self.target_position - world.global_position[:2]
-        velocity = MathOps.rotate_2d_vec(
-            raw_target,
-            -robot.global_orientation_euler[2],
-            is_rad=False
-        )
-
-        # 计算相对方向
-        rel_orientation = MathOps.vector_angle(velocity) * 0.3
-        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
-
-        velocity = np.concatenate([velocity, np.array([rel_orientation])])
-        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
-        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
-
-        # 关节状态
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        qpos_qvel_previous_action = np.concatenate([
-            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
-            radian_joint_speeds / 110.0 * self.train_sim_flip,
-            self.previous_action / 10.0,
-        ])
-
-        # 角速度
-        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
-
-        # 投影的重力方向
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-
-        # 组合观测
-        observation = np.concatenate([
-            qpos_qvel_previous_action,
-            ang_vel,
-            velocity,
-            projected_gravity,
-        ])
-
-        observation = np.clip(observation, -10.0, 10.0)
-        return observation.astype(np.float32)
-
-    def sync(self):
-        ''' Run a single simulation step '''
-        self._safe_receive_world_update(retries=1)
-        self.Player.robot.commit_motor_targets_pd()
-        self.Player.server.send()
-        if self._target_dt > 0.0:
-            now = time.time()
-            if self._last_sync_time is None:
-                self._last_sync_time = now
-                return
-            elapsed = now - self._last_sync_time
-            remaining = self._target_dt - elapsed
-            if remaining > 0.0:
-                time.sleep(remaining)
-                now = time.time()
-            self._last_sync_time = now
-
-    def debug_joint_status(self):
-        robot = self.Player.robot
-        actual_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        target_joint_positions = getattr(
-            self,
-            'target_joint_positions',
-            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
-        )
-        joint_error = actual_joint_positions - target_joint_positions
-        leg_slice = slice(11, None)
-
-        self.debug_log(
-            "[WalkDebug] "
-            f"step={self.step_counter} "
-            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
-            f"target_xy={np.round(self.target_position, 3).tolist()} "
-            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
-            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
-            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
-            f"fallen={self.Player.world.global_position[2] < 0.3}"
-        )
-        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
-
-    def reset(self, seed=None, options=None):
-        '''
-        Reset and stabilize the robot
-        Note: for some behaviors it would be better to reduce stabilization or add noise
-        '''
-        r = self.Player.robot
-        super().reset(seed=seed)
-        if seed is not None:
-            np.random.seed(seed)
-
-        target_distance = np.random.uniform(self.reset_target_distance_min, self.reset_target_distance_max)
-        target_bearing_deg = np.random.uniform(-self.reset_target_bearing_range_deg, self.reset_target_bearing_range_deg)
-
-        self.step_counter = 0
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
-        self.last_yaw_error = None
-        self.walk_cycle_step = 0
-        self._reward_debug_steps_left = 0
-
-        # 随机 beam 目标位置和朝向，增加训练多样性
-        beam_x = (random() - 0.5) * 10
-        beam_y = (random() - 0.5) * 10
-        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
-
-        for _ in range(5):
-            self._safe_receive_world_update(retries=2)
-            self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
-            self.Player.server.send()
-
-        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
-        finished_count = 0
-        for _ in range(50):
-            finished = self.Player.skills_manager.execute("Neutral")
-            self.sync()
-            if finished:
-                finished_count += 1
-                if finished_count >= 20:  # 假设需要连续20次完成才算成功
-                    break
-
-        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-            # Perturb waist + lower body only (10:), keep head/arms stable.
-            perturb_action[10:] = np.random.uniform(
-                -self.reset_joint_noise_rad,
-                self.reset_joint_noise_rad,
-                size=(self.no_of_actions - 10,)
-            )
-
-            for _ in range(self.reset_perturb_steps):
-                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-            for i in range(self.reset_recover_steps):
-                # Linearly fade perturbation to help policy start from near-neutral.
-                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-        # memory variables
-        self.sync()  
-        self.initial_position = np.array(self.Player.world.global_position[:2])
-        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions, np.float32)
-        # Randomize global target bearing so policy must learn to rotate toward it first.
-        heading_deg = float(r.global_orientation_euler[2])
-        target_offset = MathOps.rotate_2d_vec(
-            np.array([target_distance, 0.0]),
-            heading_deg + target_bearing_deg,
-            is_rad=False,
-        )
-        point1 = self.initial_position + target_offset
-        self.point_list = [point1]
-        self.target_position = self.point_list[self.waypoint_index]
-        self.initial_height = self.Player.world.global_position[2]
-
-        return self.observe(True), {}
-
-    def render(self, mode='human', close=False):
-        return
-
-    
-    def compute_reward(self, previous_pos, current_pos, action):
-        height = float(self.Player.world.global_position[2])
-        robot = self.Player.robot
-
-
-        joint_pos_rad = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        joint_speed_rad = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-        tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        ang_vel = np.deg2rad(robot.gyroscope)
-        rp_ang_vel_mag = float(np.linalg.norm(ang_vel[:2]))
-
-        is_fallen = height < 0.55
-        if is_fallen:
-            # remain = max(0, 800 - self.step_counter)
-            # return -8.0 - 0.01 * remain
-            return -20.0
-        
-
-        if np.linalg.norm(current_pos - previous_pos) > 0.005:
-            position_penalty = -3 * float(np.linalg.norm(current_pos - previous_pos))
-        else:
-            position_penalty = 0.0
-
-
-        # Turn-to-target shaping.
-        to_target = self.target_position - current_pos
-        dist_to_target = float(np.linalg.norm(to_target))
-        if dist_to_target > 1e-6:
-            target_yaw = math.atan2(float(to_target[1]), float(to_target[0]))
-        else:
-            target_yaw = 0.0
-
-        robot_yaw = math.radians(float(robot.global_orientation_euler[2]))
-        yaw_error = target_yaw - robot_yaw
-
-        # Main heading objective: face the target direction.
-        # heading_align_reward = 1.0 * math.cos(yaw_error)
-
-        abs_yaw_error = abs(yaw_error)
-        alive_bonus = 2.0 * max(0.0, 1.0 - abs_yaw_error / math.pi)
-        head_toward_bonus = self.reward_head_toward_bonus if abs_yaw_error < math.radians(4.0) else 0.0
-
-        if self.last_yaw_error is None:
-            heading_progress_reward = 0.0
-        else:
-            prev_abs_yaw_error = abs(self.last_yaw_error)
-            yaw_err_delta = prev_abs_yaw_error - abs_yaw_error
-            progress_gate = 1.0 if abs_yaw_error > math.radians(4.0) else 0.0
-            heading_progress_reward =  0.8 * progress_gate * yaw_err_delta
-            heading_progress_reward = float(np.clip(heading_progress_reward, -0.4, 0.4))
-        self.last_yaw_error = yaw_error
-
-        # action_penalty = -0.01 * float(np.linalg.norm(action))
-        smoothness_penalty = -0.05 * float(np.linalg.norm(action - self.last_action_for_reward))
-
-        posture_penalty = -0.6 * tilt_mag
-        # Penalize roll/pitch rotational shake but do not penalize yaw turning directly.
-        ang_vel_penalty = -0.06 * rp_ang_vel_mag
-
-        joint_pos = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        ) * self.train_sim_flip
-        left_hip_roll = float(joint_pos[12])
-        right_hip_roll = float(joint_pos[18])
-        left_hip_pitch = float(joint_pos[11])
-        right_hip_pitch = float(joint_pos[17])
-
-        left_ankle_roll = float(joint_pos[16])
-        right_ankle_roll = float(joint_pos[22])
-
-        max_leg_roll = 0.15  # 防止劈叉姿势
-        split_penalty = -0.8 * max(0.0, (-left_hip_roll + right_hip_roll - 2 * max_leg_roll) / max_leg_roll)
-        left_hip_yaw = float(joint_pos[13])
-        right_hip_yaw = float(joint_pos[19])
-
-        min_leg_separation = 0.05  # 最小腿间距（防止贴得太近）
-        # 惩罚腿过分靠拢（内收）- 基于两腿间距
-        leg_separation = -left_hip_roll + right_hip_roll
-        inward_penalty = -0.25 * max(0.0, (min_leg_separation - leg_separation) / min_leg_separation)
-        
-        
-        # 脚踝roll角度检测：防止过度外翻或内翻
-        max_ankle_roll = 0.15  # 最大允许的脚踝roll角度
-        
-        # 惩罚脚踝过度外翻/内翻（绝对值过大）
-        ankle_roll_penalty = -0.5 * max(0.0, (abs(left_ankle_roll) + abs(right_ankle_roll) - 2 * max_ankle_roll) / max_ankle_roll)
-        
-        # 惩罚两脚踝roll方向相反（不稳定姿势）
-        ankle_roll_cross_penalty = -0.3 * max(0.0, -(left_ankle_roll * right_ankle_roll))
-
-       # 分别惩罚左右大腿过度转动
-        max_hip_yaw = 0.3  # 最大允许的yaw角度
-        left_hip_yaw_penalty = -0.4 * max(0.0, abs(left_hip_yaw) - max_hip_yaw)
-        right_hip_yaw_penalty = -0.4 * max(0.0, abs(right_hip_yaw) - max_hip_yaw)
-        # 智能交叉腿惩罚：只在站立时惩罚，转身时允许交叉腿
-        yaw_rate = float(np.deg2rad(robot.gyroscope[2]))
-        yaw_rate_abs = abs(yaw_rate)
-
-        # 当转身速度较小时才惩罚交叉腿（站立状态）
-        cross_leg_gate = max(0.0, 1.0 - yaw_rate_abs / math.radians(8.0))
-        hip_yaw_cross_penalty = -1.0 * cross_leg_gate * max(0.0, -(left_hip_yaw * right_hip_yaw)) if left_hip_yaw > 0 and right_hip_yaw < 0 else 0.0
-
-        # Torso-lower-body linkage: reward coordinated turning, punish waist-only spinning.
-        waist_speed = abs(float(joint_speed_rad[10]))
-        lower_body_speed = float(np.mean(np.abs(joint_speed_rad[11:23])))
-        lower_body_follow_ratio = lower_body_speed / (waist_speed + 1e-4)
-        linkage_reward = 0.24 * min(1.0, lower_body_follow_ratio) * min(1.0, waist_speed / 1.2)
-        waist_only_turn_penalty = -0.20 * max(0.0, waist_speed - 1.35 * lower_body_speed)
-
-        # Extra posture linkage in yaw joints to avoid decoupled torso twist.
-        waist_yaw = abs(float(joint_pos_rad[10]))
-        hip_yaw_mean = 0.5 * (abs(float(joint_pos_rad[13])) + abs(float(joint_pos_rad[19])))
-        yaw_link_reward = 0.12 * math.exp(-abs(waist_yaw - hip_yaw_mean) / 0.22)
-
-        target_height = self.initial_height
-        height_error =  height - target_height
-        height_error = height - target_height
-        
-        height_penalty = -(math.exp(12*abs(height_error))-1) if height_error > 0.04 else 0
-
-        # # 在 compute_reward 开头附近，添加高度变化率计算
-        # if not hasattr(self, 'last_height'):
-        #     self.last_height = height
-        #     self.last_height_time = self.step_counter  # 可选，用于时间间隔
-        # height_rate = height - self.last_height  # 正为上升，负为下降
-        # self.last_height = height
-
-        # 惩罚高度下降（负变化率）
-        # height_down_penalty = -5.0 * max(0, -height_rate)  # 系数可调，-height_rate 为正表示下降幅度
-
-        # # 在 compute_reward 中
-        # if self.step_counter > 50:
-        #     avg_prev_action = np.mean(self.prev_action_history, axis=0)
-        #     novelty = float(np.linalg.norm(action - avg_prev_action))
-        #     exploration_bonus = 0.05 * novelty
-        # else:
-        #     exploration_bonus = 0
-
-        # self.prev_action_history[self.history_idx] = action
-        # self.history_idx = (self.history_idx + 1) % 50
-
-
-        total = (
-            # progress_reward  + 
-                alive_bonus + 
-                head_toward_bonus +
-                heading_progress_reward +
-                # lateral_penalty +
-                # action_penalty +
-                smoothness_penalty +
-                posture_penalty
-                + ang_vel_penalty
-                + height_penalty
-                + ankle_roll_penalty
-                + ankle_roll_cross_penalty
-                + split_penalty
-                + inward_penalty
-                # + leg_proximity_penalty
-                + left_hip_yaw_penalty
-                + right_hip_yaw_penalty
-                + hip_yaw_cross_penalty
-                + position_penalty
-                # + linkage_reward
-                # + waist_only_turn_penalty
-                # + yaw_link_reward
-                # + stance_collapse_penalty
-                # + hip_yaw_yaw_cross_penalty
-                # + stance_collapse_penalty
-                #  + cross_leg_penalty
-                #  + exploration_bonus
-                # + height_down_penalty
-                 )
-        # print(height_error, height_penalty)
-        
-        now = time.time()
-        if self.reward_debug_interval_sec > 0 and now - self._reward_debug_last_time >= self.reward_debug_interval_sec:
-            self._reward_debug_last_time = now
-            self._reward_debug_steps_left = max(1, self.reward_debug_burst_steps)
-
-        if self._reward_debug_steps_left > 0:
-            self._reward_debug_steps_left -= 1
-            self.debug_log(
-                f"height_penalty:{height_penalty:.4f},"
-                f"smoothness_penalty:{smoothness_penalty:.4f},"
-                f"posture_penalty:{posture_penalty:.4f},"
-                f"heading_progress_reward:{heading_progress_reward:.4f},"
-                # f"stance_collapse_penalty:{stance_collapse_penalty:.4f},"
-                # f"cross_leg_penalty:{cross_leg_penalty:.4f},"
-                f"ang_vel_penalty:{ang_vel_penalty:.4f},"
-                f"split_penalty:{split_penalty:.4f},"
-                f"ankle_roll_penalty:{ankle_roll_penalty:.4f},"
-                f"ankle_roll_cross_penalty:{ankle_roll_cross_penalty:.4f},"
-                f"left_hip_yaw_penalty:{left_hip_yaw_penalty:.4f},"
-                f"right_hip_yaw_penalty:{right_hip_yaw_penalty:.4f},"
-                f"hip_yaw_cross_penalty:{hip_yaw_cross_penalty:.4f},"
-                f"inward_penalty:{inward_penalty:.4f},"
-                f"position_penalty:{position_penalty:.4f},"
-                # f"linkage_reward:{linkage_reward:.4f},"
-                # f"waist_only_turn_penalty:{waist_only_turn_penalty:.4f},"
-                # f"yaw_link_reward:{yaw_link_reward:.4f}"
-                # f"leg_proximity_penalty:{leg_proximity_penalty:.4f},"
-
-                # f"stance_collapse_penalty:{stance_collapse_penalty:.4f},"
-                # f"hip_yaw_yaw_cross_penalty:{hip_yaw_yaw_cross_penalty:.4f},"
-                #   f"height_down_penalty:{height_down_penalty:.4f}",
-                #   f"exploration_bonus:{exploration_bonus:.4f}"
-                f"alive_bonus:{alive_bonus:.4f},"
-                f"abs_yaw_error:{abs_yaw_error:.4f}"
-                f"total:{total:.4f}"
-                )
-
-        return total
-
-
-
-    def step(self, action):
-
-        r = self.Player.robot
-        max_action_delta =  0.5# Limit how much the action can change from the previous step to encourage smoother motions.
-        if self.previous_action is not None:
-            action = np.clip(action, self.previous_action - max_action_delta, self.previous_action + max_action_delta)
-        action[0:2] = 0
-        action[3] = 4
-        action[7] = -4
-        action[2] = 0
-        action[6] = 0
-        action[4] = 0
-        action[5] = -5
-        action[8] = 0
-        action[9] = 5
-        action[10] = 0
-        action[11] = np.clip(action[11], -0.1, 0.1)
-        action[17] = np.clip(action[17], -0.1, 0.1)
-        # action[12] = -1.0
-        # action[18] = 1.0
-        # action[13] = -1.0
-        # action[19] = 1.0
-        self.previous_action = action.copy()
-
-        self.target_joint_positions = (
-                # self.joint_nominal_position +
-                 self.scaling_factor * action
-        )
-        self.target_joint_positions *= self.train_sim_flip
-
-        for idx, target in enumerate(self.target_joint_positions):
-            r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=110, kd=29.5
-            )
-
-        self.previous_action = action.copy()
-
-        self.sync()  # run simulation step
-        self.step_counter += 1
-
-        if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
-            self.debug_joint_status()
-
-        current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
-
-        if self.step_counter % 10 == 0:
-            self.previous_pos = current_pos.copy()
-
-        # Compute reward based on movement from previous step
-        reward = self.compute_reward(self.previous_pos, current_pos, action)
-
-        self.last_action_for_reward = action.copy()
-
-        # Fall detection and penalty
-        is_fallen = self.Player.world.global_position[2] < 0.55
-
-        # terminal state: the robot is falling or timeout
-        terminated = is_fallen or self.step_counter > 800 or self.route_completed
-        truncated = False
-
-        return self.observe(), reward, terminated, truncated, {}
-
-
-class Train(Train_Base):
-    def __init__(self, script) -> None:
-        super().__init__(script)
-
-    def train(self, args):
-
-        # --------------------------------------- Learning parameters
-        n_envs = int(os.environ.get("GYM_CPU_N_ENVS", "20"))
-        if n_envs < 1:
-            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
-        server_warmup_sec = float(os.environ.get("GYM_CPU_SERVER_WARMUP_SEC", "3.0"))
-        n_steps_per_env = int(os.environ.get("GYM_CPU_TRAIN_STEPS_PER_ENV", "512"))  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = int(os.environ.get("GYM_CPU_TRAIN_BATCH_SIZE", "512"))  # should be a factor of (n_steps_per_env * n_envs)
-        total_steps = 30000000
-        learning_rate = float(os.environ.get("GYM_CPU_TRAIN_LR", "3e-4"))
-        folder_name = f'Turn_R{self.robot_type}'
-        model_path = f'./scripts/gyms/logs/{folder_name}/'
-
-        print(f"Model path: {model_path}")
-        print(f"Using {n_envs} parallel environments")
-
-        # --------------------------------------- Run algorithm
-        def init_env(i_env, monitor=False):
-            def thunk():
-                env = WalkEnv(self.ip, self.server_p + i_env)
-                if monitor:
-                    env = Monitor(env)
-                return env
-
-            return thunk
-
-        server_log_dir = os.path.join(model_path, "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1, no_render=True, no_realtime=True)  # include 1 extra server for testing
-
-        # Wait for servers to start
-        print(f"Starting {n_envs + 1} rcssservermj servers...")
-        if server_warmup_sec > 0:
-            print(f"Waiting {server_warmup_sec:.1f}s for server warmup...")
-            sleep(server_warmup_sec)
-        print("Servers started, creating environments...")
-
-        env = SubprocVecEnv([init_env(i, monitor=True) for i in range(n_envs)], start_method="spawn")
-        # Use single-process eval env to avoid extra subprocess fragility during callback evaluation.
-        eval_env = DummyVecEnv([init_env(n_envs, monitor=True)])
-
-        try:
-            # Custom policy network architecture
-            policy_kwargs = dict(
-                net_arch=dict(
-                    pi=[512, 256, 128],  # Policy network: 3 layers
-                    vf=[512, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
-            )
-
-            if "model_file" in args:  # retrain
-                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
-                                 batch_size=minibatch_size, learning_rate=learning_rate)
-            else:  # train new model
-                model = PPO(
-                    "MlpPolicy",
-                    env=env,
-                    verbose=1,
-                    n_steps=n_steps_per_env,
-                    batch_size=minibatch_size,
-                    learning_rate=learning_rate,
-                    device="cpu",
-                    policy_kwargs=policy_kwargs,
-                    ent_coef=float(os.environ.get("GYM_CPU_TRAIN_ENT_COEF", "0.05")),  # Entropy coefficient for exploration
-                    clip_range=float(os.environ.get("GYM_CPU_TRAIN_CLIP_RANGE", "0.2")),  # PPO clipping parameter
-                    gae_lambda=0.95,  # GAE lambda
-                    gamma=float(os.environ.get("GYM_CPU_TRAIN_GAMMA", "0.95")), # Discount factor
-                    # target_kl=0.03,
-                    n_epochs=int(os.environ.get("GYM_CPU_TRAIN_EPOCHS", "5")),
-                    tensorboard_log=f"./scripts/gyms/logs/{folder_name}/tensorboard/"
-                )
-
-            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
-                                          eval_freq=n_steps_per_env * 20, save_freq=n_steps_per_env * 20, eval_eps=7,
-                                          backup_env_file=__file__)
-        except KeyboardInterrupt:
-            sleep(1)  # wait for child processes
-            print("\nctrl+c pressed, aborting...\n")
-            servers.kill()
-            return
-
-        env.close()
-        eval_env.close()
-        servers.kill()
-
-    def test(self, args):
-
-        # Uses different server and monitor ports
-        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        test_no_render = os.environ.get("GYM_CPU_TEST_NO_RENDER", "0") == "1"
-        test_no_realtime = os.environ.get("GYM_CPU_TEST_NO_REALTIME", "0") == "1"
-
-        server = Train_Server(
-            self.server_p - 1,
-            self.monitor_p,
-            1,
-            no_render=test_no_render,
-            no_realtime=test_no_realtime,
-        )
-        env = WalkEnv(self.ip, self.server_p - 1)
-        model = PPO.load(args["model_file"], env=env)
-
-        try:
-            self.export_model(args["model_file"], args["model_file"] + ".pkl",
-                              False)  # Export to pkl to create custom behavior
-            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
-        except KeyboardInterrupt:
-            print()
-
-        env.close()
-        server.kill()
-
-
-if __name__ == "__main__":
-    from types import SimpleNamespace
-
-    # 创建默认参数
-    script_args = SimpleNamespace(
-        args=SimpleNamespace(
-            i='127.0.0.1',  # Server IP
-            p=3100,  # Server port
-            m=3200,  # Monitor port
-            r=0,  # Robot type
-            t='Gym',  # Team name
-            u=1  # Uniform number
-        )
-    )
-
-    trainer = Train(script_args)
-
-    run_mode = os.environ.get("GYM_CPU_MODE", "train").strip().lower()
-
-    if run_mode == "test":
-        test_model_file = os.environ.get("GYM_CPU_TEST_MODEL", "scripts/gyms/logs/Turn_R0_004/best_model.zip")
-        test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Turn_R0_004/")
-        trainer.test({"model_file": test_model_file, "folder_dir": test_folder})
-    else:
-        retrain_model = os.environ.get("GYM_CPU_TRAIN_MODEL", "").strip()
-        if retrain_model:
-            trainer.train({"model_file": retrain_model})
-        else:
-            trainer.train({})

scripts/gyms/logs/Walk_R0_000/Walk.py

@@ -1,624 +0,0 @@
-import os
-import numpy as np
-import math
-import time
-from time import sleep
-from random import random
-from random import uniform
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.vec_env import SubprocVecEnv
-
-import gymnasium as gym
-from gymnasium import spaces
-
-from scripts.commons.Train_Base import Train_Base
-from scripts.commons.Server import Server as Train_Server
-
-from agent.base_agent import Base_Agent
-from utils.math_ops import MathOps
-
-from scipy.spatial.transform import Rotation as R
-
-'''
-Objective:
-Learn how to run forward using step primitive
-----------
-- class Basic_Run: implements an OpenAI custom gym
-- class Train:  implements algorithms to train a new model or test an existing model
-'''
-
-
-class WalkEnv(gym.Env):
-    def __init__(self, ip, server_p) -> None:
-
-        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
-        self.Player = player = Base_Agent(
-            team_name="Gym",
-            number=1,
-            host=ip,
-            port=server_p
-        )
-        self.robot_type = self.Player.robot
-        self.step_counter = 0  # to limit episode size
-        self.force_play_on = True
-
-        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
-        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
-        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
-        self.isfallen = False
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.debug_every_n_steps = 5
-        self.calibrate_nominal_from_neutral = True
-        self.auto_calibrate_train_sim_flip = True
-        self.nominal_calibrated_once = False
-        self.flip_calibrated_once = False
-        self._target_hz = 0.0
-        self._target_dt = 0.0
-        self._last_sync_time = None
-        target_hz_env = 0
-        if target_hz_env:
-            try:
-                self._target_hz = float(target_hz_env)
-            except ValueError:
-                self._target_hz = 0.0
-        if self._target_hz > 0.0:
-            self._target_dt = 1.0 / self._target_hz
-
-        # State space
-        # 原始观测大小: 78
-        obs_size = 78
-        self.obs = np.zeros(obs_size, np.float32)
-        self.observation_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(obs_size,),
-            dtype=np.float32
-        )
-
-        action_dim = len(self.Player.robot.ROBOT_MOTORS)
-        self.no_of_actions = action_dim
-        self.action_space = spaces.Box(
-            low=-1.0,
-            high=1.0,
-            shape=(action_dim,),
-            dtype=np.float32
-        )
-
-        # 中立姿态
-        self.joint_nominal_position = np.array(
-            [
-                0.0,
-                0.0,
-                0.0,
-                1.4,
-                0.0,
-                -0.4,
-                0.0,
-                -1.4,
-                0.0,
-                0.4,
-                0.0,
-                -0.4,
-                0.0,
-                0.0,
-                0.8,
-                -0.4,
-                0.0,
-                0.4,
-                0.0,
-                0.0,
-                -0.8,
-                0.4,
-                0.0,
-            ]
-        )
-        self.joint_nominal_position = np.zeros(self.no_of_actions)
-        self.train_sim_flip = np.array(
-            [
-                1.0,  # 0: Head_yaw (he1)
-                -1.0,  # 1: Head_pitch (he2)
-                1.0,  # 2: Left_Shoulder_Pitch (lae1)
-                -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                -1.0,  # 4: Left_Elbow_Pitch (lae3)
-                1.0,  # 5: Left_Elbow_Yaw (lae4)
-                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                -1.0,  # 7: Right_Shoulder_Roll (rae2)
-                1.0,  # 8: Right_Elbow_Pitch (rae3)
-                1.0,  # 9: Right_Elbow_Yaw (rae4)
-                1.0,  # 10: Waist (te1)
-                1.0,  # 11: Left_Hip_Pitch (lle1)
-                -1.0,  # 12: Left_Hip_Roll (lle2)
-                -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,  # 14: Left_Knee_Pitch (lle4)
-                1.0,  # 15: Left_Ankle_Pitch (lle5)
-                -1.0,  # 16: Left_Ankle_Roll (lle6)
-                -1.0,  # 17: Right_Hip_Pitch (rle1)
-                -1.0,  # 18: Right_Hip_Roll (rle2)
-                -1.0,  # 19: Right_Hip_Yaw (rle3)
-                -1.0,  # 20: Right_Knee_Pitch (rle4)
-                -1.0,  # 21: Right_Ankle_Pitch (rle5)
-                -1.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-
-        self.scaling_factor = 0.5
-        # self.scaling_factor = 1
-
-        # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = False
-        self.reset_beam_yaw_range_deg = 180  # randomize target direction fully to encourage learning a real walk instead of a fixed gait
-        self.reset_joint_noise_rad = 0.035
-        self.reset_perturb_steps = 5
-        self.reset_recover_steps = 8
-
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Track previous position
-        self.Player.server.connect()
-        # sleep(2.0)  # Longer wait for connection to establish completely
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-        self.start_time = time.time()
-
-    def debug_log(self, message):
-        print(message)
-        try:
-            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
-            with open(log_path, "a", encoding="utf-8") as f:
-                f.write(message + "\n")
-        except OSError:
-            pass
-
-    def observe(self, init=False):
-
-        """获取当前观测值"""
-        robot = self.Player.robot
-        world = self.Player.world
-
-        # Safety check: ensure data is available
-
-        # 计算目标速度
-        raw_target = self.target_position - world.global_position[:2]
-        velocity = MathOps.rotate_2d_vec(
-            raw_target,
-            -robot.global_orientation_euler[2],
-            is_rad=False
-        )
-
-        # 计算相对方向
-        rel_orientation = MathOps.vector_angle(velocity) * 0.3
-        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
-
-        velocity = np.concatenate([velocity, np.array([rel_orientation])])
-        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
-        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
-
-        # 关节状态
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        qpos_qvel_previous_action = np.concatenate([
-            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
-            radian_joint_speeds / 110.0 * self.train_sim_flip,
-            self.previous_action / 10.0,
-        ])
-
-        # 角速度
-        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
-
-        # 投影的重力方向
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-
-        # 组合观测
-        observation = np.concatenate([
-            qpos_qvel_previous_action,
-            ang_vel,
-            velocity,
-            projected_gravity,
-        ])
-
-        observation = np.clip(observation, -10.0, 10.0)
-        return observation.astype(np.float32)
-
-    def sync(self):
-        ''' Run a single simulation step '''
-        self.Player.server.receive()
-        self.Player.world.update()
-        self.Player.robot.commit_motor_targets_pd()
-        self.Player.server.send()
-        if self._target_dt > 0.0:
-            now = time.time()
-            if self._last_sync_time is None:
-                self._last_sync_time = now
-                return
-            elapsed = now - self._last_sync_time
-            remaining = self._target_dt - elapsed
-            if remaining > 0.0:
-                time.sleep(remaining)
-                now = time.time()
-            self._last_sync_time = now
-
-    def debug_joint_status(self):
-        robot = self.Player.robot
-        actual_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        target_joint_positions = getattr(
-            self,
-            'target_joint_positions',
-            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
-        )
-        joint_error = actual_joint_positions - target_joint_positions
-        leg_slice = slice(11, None)
-
-        self.debug_log(
-            "[WalkDebug] "
-            f"step={self.step_counter} "
-            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
-            f"target_xy={np.round(self.target_position, 3).tolist()} "
-            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
-            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
-            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
-            f"fallen={self.Player.world.global_position[2] < 0.3}"
-        )
-        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
-
-    def reset(self, seed=None, options=None):
-        '''
-        Reset and stabilize the robot
-        Note: for some behaviors it would be better to reduce stabilization or add noise
-        '''
-        r = self.Player.robot
-        super().reset(seed=seed)
-        if seed is not None:
-            np.random.seed(seed)
-
-        length1 = 2  # randomize target distance
-        length2 = np.random.uniform(0.6, 1)  # randomize target distance
-        length3 = np.random.uniform(0.6, 1)  # randomize target distance
-        angle2 = np.random.uniform(-30, 30)  # randomize initial orientation
-        angle3 = np.random.uniform(-30, 30)  # randomize target direction
-
-        self.step_counter = 0
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
-        self.walk_cycle_step = 0
-
-        # 随机 beam 目标位置和朝向，增加训练多样性
-        beam_x = (random() - 0.5) * 10
-        beam_y = (random() - 0.5) * 10
-        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
-
-        for _ in range(5):
-            self.Player.server.receive()
-            self.Player.world.update()
-            self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
-            self.Player.server.send()
-
-        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
-        finished_count = 0
-        for _ in range(50):
-            finished = self.Player.skills_manager.execute("Neutral")
-            self.sync()
-            if finished:
-                finished_count += 1
-                if finished_count >= 20:  # 假设需要连续20次完成才算成功
-                    break
-
-        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-            # Perturb waist + lower body only (10:), keep head/arms stable.
-            perturb_action[10:] = np.random.uniform(
-                -self.reset_joint_noise_rad,
-                self.reset_joint_noise_rad,
-                size=(self.no_of_actions - 10,)
-            )
-
-            for _ in range(self.reset_perturb_steps):
-                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-            for i in range(self.reset_recover_steps):
-                # Linearly fade perturbation to help policy start from near-neutral.
-                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-        # memory variables
-        self.initial_position = np.array(self.Player.world.global_position[:2])
-        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions, np.float32)
-        # Build target in the robot's current forward direction instead of fixed global +x.
-        heading_deg = float(r.global_orientation_euler[2])
-        forward_offset = MathOps.rotate_2d_vec(np.array([length1, 0.0]), heading_deg, is_rad=False)
-        point1 = self.initial_position + forward_offset
-        point2 = point1 + MathOps.rotate_2d_vec(np.array([length2, 0]), angle2, is_rad=False)
-        point3 = point2 + MathOps.rotate_2d_vec(np.array([length3, 0]), angle3, is_rad=False)
-        self.point_list = [point1]
-        self.target_position = self.point_list[self.waypoint_index]
-        self.initial_height = self.Player.world.global_position[2]
-
-        return self.observe(True), {}
-
-    def render(self, mode='human', close=False):
-        return
-
-    def compute_reward(self, previous_pos, current_pos, action):
-        height = float(self.Player.world.global_position[2])
-
-        orientation_quat_inv = R.from_quat(self.Player.robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-        tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        ang_vel = np.deg2rad(self.Player.robot.gyroscope)
-        ang_vel_mag = float(np.linalg.norm(ang_vel))
-
-        # 摔倒检测（重要！）
-        if height < 0.3:
-            if time.time() - self.start_time > 1200:
-                self.start_time = time.time()
-                print("fall_penalty: -20")
-            return -20.0
-        
-
-
-        # # 目标方向
-        # to_target = self.target_position - current_pos
-        # dist_to_target = float(np.linalg.norm(to_target))
-        # if dist_to_target < 0.5:
-        #     return 15.0
-
-        # forward_dir = to_target / dist_to_target if dist_to_target > 0.1 else np.array([1.0, 0.0])
-        # delta_pos = current_pos - previous_pos
-        # forward_step = float(np.dot(delta_pos, forward_dir))
-        # lateral_step = float(np.linalg.norm(delta_pos - forward_dir * forward_step))
-
-        # 奖励项
-        # progress_reward = 2 * forward_step
-        # lateral_penalty = -0.1 * lateral_step
-        alive_bonus = 0.1
-
-        # action_penalty = -0.01 * float(np.linalg.norm(action))
-        smoothness_penalty = -0.05 * float(np.linalg.norm(action - self.last_action_for_reward))
-
-        posture_penalty = -1.0 * (tilt_mag)
-        # ang_vel_penalty = -0.05 * ang_vel_mag
-
-        target_height = self.initial_height
-        height_error =  height - target_height if abs(height - target_height) > 0.05 else 0.0
-        height_penalty = -2.0 * abs(height_error)  # 惩罚高度偏离，系数可调
-
-        # # 在 compute_reward 开头附近，添加高度变化率计算
-        # if not hasattr(self, 'last_height'):
-        #     self.last_height = height
-        #     self.last_height_time = self.step_counter  # 可选，用于时间间隔
-        # height_rate = height - self.last_height  # 正为上升，负为下降
-        # self.last_height = height
-
-        # 惩罚高度下降（负变化率）
-        # height_down_penalty = -5.0 * max(0, -height_rate)  # 系数可调，-height_rate 为正表示下降幅度
-
-        # # 在 compute_reward 中
-        # if self.step_counter > 50:
-        #     avg_prev_action = np.mean(self.prev_action_history, axis=0)
-        #     novelty = float(np.linalg.norm(action - avg_prev_action))
-        #     exploration_bonus = 0.05 * novelty
-        # else:
-        #     exploration_bonus = 0
-
-        # self.prev_action_history[self.history_idx] = action
-        # self.history_idx = (self.history_idx + 1) % 50
-
-
-        total = (
-            # progress_reward  + 
-                 alive_bonus + 
-                 # lateral_penalty +
-                 # action_penalty +
-                smoothness_penalty +
-                 posture_penalty
-                #  + ang_vel_penalty
-                 + height_penalty
-                #  + exploration_bonus
-                # + height_down_penalty
-                 )
-        if time.time() - self.start_time >= 1200:
-            self.start_time = time.time()
-            print(
-                #   f"progress_reward:{progress_reward:.4f}",
-                #   f"lateral_penalty:{lateral_penalty:.4f}",
-                #   f"action_penalty:{action_penalty:.4f}"s, 
-                  f"height_penalty:{height_penalty:.4f}",
-                  f"smoothness_penalty:{smoothness_penalty:.4f},",
-                  f"posture_penalty:{posture_penalty:.4f}",
-                #   f"ang_vel_penalty:{ang_vel_penalty:.4f}",
-                #   f"height_down_penalty:{height_down_penalty:.4f}",
-                #   f"exploration_bonus:{exploration_bonus:.4f}"
-            )
-
-        return total
-
-
-
-    def step(self, action):
-
-        r = self.Player.robot
-        self.previous_action = action
-
-        self.target_joint_positions = (
-                self.joint_nominal_position
-                + self.scaling_factor * action
-        )
-        self.target_joint_positions *= self.train_sim_flip
-
-        for idx, target in enumerate(self.target_joint_positions):
-            r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-            )
-
-        self.previous_action = action
-
-        self.sync()  # run simulation step
-        self.step_counter += 1
-
-        # self.debug_joint_status()
-
-        current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
-
-        # Compute reward based on movement from previous step
-        reward = self.compute_reward(self.previous_pos, current_pos, action)
-
-        # Update previous position
-        self.previous_pos = current_pos.copy()
-        self.last_action_for_reward = action.copy()
-
-        # Fall detection and penalty
-        is_fallen = self.Player.world.global_position[2] < 0.3
-
-        # terminal state: the robot is falling or timeout
-        terminated = is_fallen or self.step_counter > 800 or self.route_completed
-        truncated = False
-
-        return self.observe(), reward, terminated, truncated, {}
-
-
-class Train(Train_Base):
-    def __init__(self, script) -> None:
-        super().__init__(script)
-
-    def train(self, args):
-
-        # --------------------------------------- Learning parameters
-        n_envs = 20  # Reduced from 8 to decrease CPU/network pressure during init
-        if n_envs < 1:
-            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
-        n_steps_per_env = 1024  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = 128  # should be a factor of (n_steps_per_env * n_envs)
-        total_steps = 30000000
-        learning_rate = 3e-4
-        folder_name = f'Walk_R{self.robot_type}'
-        model_path = f'./scripts/gyms/logs/{folder_name}/'
-
-        print(f"Model path: {model_path}")
-        print(f"Using {n_envs} parallel environments")
-
-        # --------------------------------------- Run algorithm
-        def init_env(i_env):
-            def thunk():
-                return WalkEnv(self.ip, self.server_p + i_env)
-
-            return thunk
-
-        server_log_dir = os.path.join(model_path, "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1)  # include 1 extra server for testing
-
-        # Wait for servers to start
-        print(f"Starting {n_envs + 1} rcssservermj servers...")
-        print("Servers started, creating environments...")
-
-        env = SubprocVecEnv([init_env(i) for i in range(n_envs)])
-        eval_env = SubprocVecEnv([init_env(n_envs)])
-
-        try:
-            # Custom policy network architecture
-            policy_kwargs = dict(
-                net_arch=dict(
-                    pi=[512, 256, 128],  # Policy network: 3 layers
-                    vf=[512, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
-            )
-
-            if "model_file" in args:  # retrain
-                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
-                                 batch_size=minibatch_size, learning_rate=learning_rate)
-            else:  # train new model
-                model = PPO(
-                    "MlpPolicy",
-                    env=env,
-                    verbose=1,
-                    n_steps=n_steps_per_env,
-                    batch_size=minibatch_size,
-                    learning_rate=learning_rate,
-                    device="cpu",
-                    policy_kwargs=policy_kwargs,
-                    ent_coef=0.005,  # Entropy coefficient for exploration
-                    # clip_range=0.13,  # PPO clipping parameter
-                    gae_lambda=0.95,  # GAE lambda
-                    gamma=0.99 , # Discount factor
-                    # target_kl=0.03,
-                    # n_epochs=5
-                )
-
-            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
-                                          eval_freq=n_steps_per_env * 10, save_freq=n_steps_per_env * 10,
-                                          backup_env_file=__file__)
-        except KeyboardInterrupt:
-            sleep(1)  # wait for child processes
-            print("\nctrl+c pressed, aborting...\n")
-            servers.kill()
-            return
-
-        env.close()
-        eval_env.close()
-        servers.kill()
-
-    def test(self, args):
-
-        # Uses different server and monitor ports
-        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        server = Train_Server(self.server_p - 1, self.monitor_p, 1)
-        env = WalkEnv(self.ip, self.server_p - 1)
-        model = PPO.load(args["model_file"], env=env)
-
-        try:
-            self.export_model(args["model_file"], args["model_file"] + ".pkl",
-                              False)  # Export to pkl to create custom behavior
-            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
-        except KeyboardInterrupt:
-            print()
-
-        env.close()
-        server.kill()
-
-
-if __name__ == "__main__":
-    from types import SimpleNamespace
-
-    # 创建默认参数
-    script_args = SimpleNamespace(
-        args=SimpleNamespace(
-            i='127.0.0.1',  # Server IP
-            p=3100,  # Server port
-            m=3200,  # Monitor port
-            r=0,  # Robot type
-            t='Gym',  # Team name
-            u=1  # Uniform number
-        )
-    )
-
-    trainer = Train(script_args)
-    trainer.train({})
-    # trainer.test({"model_file": "scripts/gyms/logs/Walk_R0_012/best_model.zip",
-                #   "folder_dir": "scripts/gyms/logs/Walk_R0_012/",})

scripts/gyms/logs/Walk_R0_001/Walk.py

@@ -1,625 +0,0 @@
-import os
-import numpy as np
-import math
-import time
-from time import sleep
-from random import random
-from random import uniform
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.vec_env import SubprocVecEnv
-
-import gymnasium as gym
-from gymnasium import spaces
-
-from scripts.commons.Train_Base import Train_Base
-from scripts.commons.Server import Server as Train_Server
-
-from agent.base_agent import Base_Agent
-from utils.math_ops import MathOps
-
-from scipy.spatial.transform import Rotation as R
-
-'''
-Objective:
-Learn how to run forward using step primitive
-----------
-- class Basic_Run: implements an OpenAI custom gym
-- class Train:  implements algorithms to train a new model or test an existing model
-'''
-
-
-class WalkEnv(gym.Env):
-    def __init__(self, ip, server_p) -> None:
-
-        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
-        self.Player = player = Base_Agent(
-            team_name="Gym",
-            number=1,
-            host=ip,
-            port=server_p
-        )
-        self.robot_type = self.Player.robot
-        self.step_counter = 0  # to limit episode size
-        self.force_play_on = True
-
-        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
-        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
-        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
-        self.isfallen = False
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.debug_every_n_steps = 5
-        self.enable_debug_joint_status = False
-        self.calibrate_nominal_from_neutral = True
-        self.auto_calibrate_train_sim_flip = True
-        self.nominal_calibrated_once = False
-        self.flip_calibrated_once = False
-        self._target_hz = 0.0
-        self._target_dt = 0.0
-        self._last_sync_time = None
-        target_hz_env = 0
-        if target_hz_env:
-            try:
-                self._target_hz = float(target_hz_env)
-            except ValueError:
-                self._target_hz = 0.0
-        if self._target_hz > 0.0:
-            self._target_dt = 1.0 / self._target_hz
-
-        # State space
-        # 原始观测大小: 78
-        obs_size = 78
-        self.obs = np.zeros(obs_size, np.float32)
-        self.observation_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(obs_size,),
-            dtype=np.float32
-        )
-
-        action_dim = len(self.Player.robot.ROBOT_MOTORS)
-        self.no_of_actions = action_dim
-        self.action_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(action_dim,),
-            dtype=np.float32
-        )
-
-        # 中立姿态
-        self.joint_nominal_position = np.array(
-            [
-                0.0,
-                0.0,
-                0.0,
-                1.4,
-                0.0,
-                -0.4,
-                0.0,
-                -1.4,
-                0.0,
-                0.4,
-                0.0,
-                -0.4,
-                0.0,
-                0.0,
-                0.8,
-                -0.4,
-                0.0,
-                0.4,
-                0.0,
-                0.0,
-                -0.8,
-                0.4,
-                0.0,
-            ]
-        )
-        self.joint_nominal_position = np.zeros(self.no_of_actions)
-        self.train_sim_flip = np.array(
-            [
-                1.0,  # 0: Head_yaw (he1)
-                -1.0,  # 1: Head_pitch (he2)
-                1.0,  # 2: Left_Shoulder_Pitch (lae1)
-                -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                -1.0,  # 4: Left_Elbow_Pitch (lae3)
-                1.0,  # 5: Left_Elbow_Yaw (lae4)
-                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                -1.0,  # 7: Right_Shoulder_Roll (rae2)
-                1.0,  # 8: Right_Elbow_Pitch (rae3)
-                1.0,  # 9: Right_Elbow_Yaw (rae4)
-                1.0,  # 10: Waist (te1)
-                1.0,  # 11: Left_Hip_Pitch (lle1)
-                -1.0,  # 12: Left_Hip_Roll (lle2)
-                -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,  # 14: Left_Knee_Pitch (lle4)
-                1.0,  # 15: Left_Ankle_Pitch (lle5)
-                -1.0,  # 16: Left_Ankle_Roll (lle6)
-                -1.0,  # 17: Right_Hip_Pitch (rle1)
-                -1.0,  # 18: Right_Hip_Roll (rle2)
-                -1.0,  # 19: Right_Hip_Yaw (rle3)
-                -1.0,  # 20: Right_Knee_Pitch (rle4)
-                -1.0,  # 21: Right_Ankle_Pitch (rle5)
-                -1.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-
-        self.scaling_factor = 0.1
-        # self.scaling_factor = 1
-
-        # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = False
-        self.reset_beam_yaw_range_deg = 180  # randomize target direction fully to encourage learning a real walk instead of a fixed gait
-        self.reset_joint_noise_rad = 0.035
-        self.reset_perturb_steps = 5
-        self.reset_recover_steps = 8
-
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Track previous position
-        self.Player.server.connect()
-        # sleep(2.0)  # Longer wait for connection to establish completely
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-        self.start_time = time.time()
-
-    def debug_log(self, message):
-        print(message)
-        try:
-            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
-            with open(log_path, "a", encoding="utf-8") as f:
-                f.write(message + "\n")
-        except OSError:
-            pass
-
-    def observe(self, init=False):
-
-        """获取当前观测值"""
-        robot = self.Player.robot
-        world = self.Player.world
-
-        # Safety check: ensure data is available
-
-        # 计算目标速度
-        raw_target = self.target_position - world.global_position[:2]
-        velocity = MathOps.rotate_2d_vec(
-            raw_target,
-            -robot.global_orientation_euler[2],
-            is_rad=False
-        )
-
-        # 计算相对方向
-        rel_orientation = MathOps.vector_angle(velocity) * 0.3
-        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
-
-        velocity = np.concatenate([velocity, np.array([rel_orientation])])
-        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
-        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
-
-        # 关节状态
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        qpos_qvel_previous_action = np.concatenate([
-            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
-            radian_joint_speeds / 110.0 * self.train_sim_flip,
-            self.previous_action / 10.0,
-        ])
-
-        # 角速度
-        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
-
-        # 投影的重力方向
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-
-        # 组合观测
-        observation = np.concatenate([
-            qpos_qvel_previous_action,
-            ang_vel,
-            velocity,
-            projected_gravity,
-        ])
-
-        observation = np.clip(observation, -10.0, 10.0)
-        return observation.astype(np.float32)
-
-    def sync(self):
-        ''' Run a single simulation step '''
-        self.Player.server.receive()
-        self.Player.world.update()
-        self.Player.robot.commit_motor_targets_pd()
-        self.Player.server.send()
-        if self._target_dt > 0.0:
-            now = time.time()
-            if self._last_sync_time is None:
-                self._last_sync_time = now
-                return
-            elapsed = now - self._last_sync_time
-            remaining = self._target_dt - elapsed
-            if remaining > 0.0:
-                time.sleep(remaining)
-                now = time.time()
-            self._last_sync_time = now
-
-    def debug_joint_status(self):
-        robot = self.Player.robot
-        actual_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        target_joint_positions = getattr(
-            self,
-            'target_joint_positions',
-            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
-        )
-        joint_error = actual_joint_positions - target_joint_positions
-        leg_slice = slice(11, None)
-
-        self.debug_log(
-            "[WalkDebug] "
-            f"step={self.step_counter} "
-            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
-            f"target_xy={np.round(self.target_position, 3).tolist()} "
-            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
-            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
-            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
-            f"fallen={self.Player.world.global_position[2] < 0.3}"
-        )
-        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
-
-    def reset(self, seed=None, options=None):
-        '''
-        Reset and stabilize the robot
-        Note: for some behaviors it would be better to reduce stabilization or add noise
-        '''
-        r = self.Player.robot
-        super().reset(seed=seed)
-        if seed is not None:
-            np.random.seed(seed)
-
-        length1 = 2  # randomize target distance
-        length2 = np.random.uniform(0.6, 1)  # randomize target distance
-        length3 = np.random.uniform(0.6, 1)  # randomize target distance
-        angle2 = np.random.uniform(-30, 30)  # randomize initial orientation
-        angle3 = np.random.uniform(-30, 30)  # randomize target direction
-
-        self.step_counter = 0
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
-        self.walk_cycle_step = 0
-
-        # 随机 beam 目标位置和朝向，增加训练多样性
-        beam_x = (random() - 0.5) * 10
-        beam_y = (random() - 0.5) * 10
-        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
-
-        for _ in range(5):
-            self.Player.server.receive()
-            self.Player.world.update()
-            self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
-            self.Player.server.send()
-
-        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
-        finished_count = 0
-        for _ in range(50):
-            finished = self.Player.skills_manager.execute("Neutral")
-            self.sync()
-            if finished:
-                finished_count += 1
-                if finished_count >= 20:  # 假设需要连续20次完成才算成功
-                    break
-
-        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-            # Perturb waist + lower body only (10:), keep head/arms stable.
-            perturb_action[10:] = np.random.uniform(
-                -self.reset_joint_noise_rad,
-                self.reset_joint_noise_rad,
-                size=(self.no_of_actions - 10,)
-            )
-
-            for _ in range(self.reset_perturb_steps):
-                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-            for i in range(self.reset_recover_steps):
-                # Linearly fade perturbation to help policy start from near-neutral.
-                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-        # memory variables
-        self.sync()  
-        self.initial_position = np.array(self.Player.world.global_position[:2])
-        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions, np.float32)
-        # Build target in the robot's current forward direction instead of fixed global +x.
-        heading_deg = float(r.global_orientation_euler[2])
-        forward_offset = MathOps.rotate_2d_vec(np.array([length1, 0.0]), heading_deg, is_rad=False)
-        point1 = self.initial_position + forward_offset
-        point2 = point1 + MathOps.rotate_2d_vec(np.array([length2, 0]), angle2, is_rad=False)
-        point3 = point2 + MathOps.rotate_2d_vec(np.array([length3, 0]), angle3, is_rad=False)
-        self.point_list = [point1]
-        self.target_position = self.point_list[self.waypoint_index]
-        self.initial_height = self.Player.world.global_position[2]
-
-        return self.observe(True), {}
-
-    def render(self, mode='human', close=False):
-        return
-
-    def compute_reward(self, previous_pos, current_pos, action):
-        height = float(self.Player.world.global_position[2])
-
-        orientation_quat_inv = R.from_quat(self.Player.robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-        tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        ang_vel = np.deg2rad(self.Player.robot.gyroscope)
-        ang_vel_mag = float(np.linalg.norm(ang_vel))
-
-        is_fallen = height < 0.3
-        if is_fallen:
-            remain = max(0, 800 - self.step_counter)
-            return -8.0 - 0.01 * remain
-        
-
-
-        # # 目标方向
-        # to_target = self.target_position - current_pos
-        # dist_to_target = float(np.linalg.norm(to_target))
-        # if dist_to_target < 0.5:
-        #     return 15.0
-
-        # forward_dir = to_target / dist_to_target if dist_to_target > 0.1 else np.array([1.0, 0.0])
-        # delta_pos = current_pos - previous_pos
-        # forward_step = float(np.dot(delta_pos, forward_dir))
-        # lateral_step = float(np.linalg.norm(delta_pos - forward_dir * forward_step))
-
-        # 奖励项
-        # progress_reward = 2 * forward_step
-        # lateral_penalty = -0.1 * lateral_step
-        alive_bonus = 0.3
-
-        # action_penalty = -0.01 * float(np.linalg.norm(action))
-        smoothness_penalty = -0.03 * float(np.linalg.norm(action - self.last_action_for_reward))
-
-        posture_penalty = -1.0 * (tilt_mag)
-        # ang_vel_penalty = -0.05 * ang_vel_mag
-
-        target_height = self.initial_height
-        height_error =  height - target_height if abs(height - target_height) > 0.05 else 0.0
-        height_penalty = -2.0 * abs(height_error)  # 惩罚高度偏离，系数可调
-
-        # # 在 compute_reward 开头附近，添加高度变化率计算
-        # if not hasattr(self, 'last_height'):
-        #     self.last_height = height
-        #     self.last_height_time = self.step_counter  # 可选，用于时间间隔
-        # height_rate = height - self.last_height  # 正为上升，负为下降
-        # self.last_height = height
-
-        # 惩罚高度下降（负变化率）
-        # height_down_penalty = -5.0 * max(0, -height_rate)  # 系数可调，-height_rate 为正表示下降幅度
-
-        # # 在 compute_reward 中
-        # if self.step_counter > 50:
-        #     avg_prev_action = np.mean(self.prev_action_history, axis=0)
-        #     novelty = float(np.linalg.norm(action - avg_prev_action))
-        #     exploration_bonus = 0.05 * novelty
-        # else:
-        #     exploration_bonus = 0
-
-        # self.prev_action_history[self.history_idx] = action
-        # self.history_idx = (self.history_idx + 1) % 50
-
-
-        total = (
-            # progress_reward  + 
-                 alive_bonus + 
-                 # lateral_penalty +
-                 # action_penalty +
-                smoothness_penalty +
-                 posture_penalty
-                #  + ang_vel_penalty
-                 + height_penalty
-                #  + exploration_bonus
-                # + height_down_penalty
-                 )
-        if time.time() - self.start_time >= 1200:
-            self.start_time = time.time()
-            print(
-                #   f"progress_reward:{progress_reward:.4f}",
-                #   f"lateral_penalty:{lateral_penalty:.4f}",
-                #   f"action_penalty:{action_penalty:.4f}"s, 
-                  f"height_penalty:{height_penalty:.4f}",
-                  f"smoothness_penalty:{smoothness_penalty:.4f},",
-                  f"posture_penalty:{posture_penalty:.4f}",
-                #   f"ang_vel_penalty:{ang_vel_penalty:.4f}",
-                #   f"height_down_penalty:{height_down_penalty:.4f}",
-                #   f"exploration_bonus:{exploration_bonus:.4f}"
-            )
-
-        return total
-
-
-
-    def step(self, action):
-
-        r = self.Player.robot
-        self.previous_action = action
-
-        self.target_joint_positions = (
-                # self.joint_nominal_position +
-                 self.scaling_factor * action
-        )
-        self.target_joint_positions *= self.train_sim_flip
-
-        for idx, target in enumerate(self.target_joint_positions):
-            r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-            )
-
-        self.previous_action = action
-
-        self.sync()  # run simulation step
-        self.step_counter += 1
-
-        if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
-            self.debug_joint_status()
-
-        current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
-
-        # Compute reward based on movement from previous step
-        reward = self.compute_reward(self.previous_pos, current_pos, action)
-
-        # Update previous position
-        self.previous_pos = current_pos.copy()
-        self.last_action_for_reward = action.copy()
-
-        # Fall detection and penalty
-        is_fallen = self.Player.world.global_position[2] < 0.3
-
-        # terminal state: the robot is falling or timeout
-        terminated = is_fallen or self.step_counter > 800 or self.route_completed
-        truncated = False
-
-        return self.observe(), reward, terminated, truncated, {}
-
-
-class Train(Train_Base):
-    def __init__(self, script) -> None:
-        super().__init__(script)
-
-    def train(self, args):
-
-        # --------------------------------------- Learning parameters
-        n_envs = 20  # Reduced from 8 to decrease CPU/network pressure during init
-        if n_envs < 1:
-            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
-        n_steps_per_env = 1024  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = 128  # should be a factor of (n_steps_per_env * n_envs)
-        total_steps = 30000000
-        learning_rate = 3e-4
-        folder_name = f'Walk_R{self.robot_type}'
-        model_path = f'./scripts/gyms/logs/{folder_name}/'
-
-        print(f"Model path: {model_path}")
-        print(f"Using {n_envs} parallel environments")
-
-        # --------------------------------------- Run algorithm
-        def init_env(i_env):
-            def thunk():
-                return WalkEnv(self.ip, self.server_p + i_env)
-
-            return thunk
-
-        server_log_dir = os.path.join(model_path, "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1)  # include 1 extra server for testing
-
-        # Wait for servers to start
-        print(f"Starting {n_envs + 1} rcssservermj servers...")
-        print("Servers started, creating environments...")
-
-        env = SubprocVecEnv([init_env(i) for i in range(n_envs)])
-        eval_env = SubprocVecEnv([init_env(n_envs)])
-
-        try:
-            # Custom policy network architecture
-            policy_kwargs = dict(
-                net_arch=dict(
-                    pi=[512, 256, 128],  # Policy network: 3 layers
-                    vf=[512, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
-            )
-
-            if "model_file" in args:  # retrain
-                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
-                                 batch_size=minibatch_size, learning_rate=learning_rate)
-            else:  # train new model
-                model = PPO(
-                    "MlpPolicy",
-                    env=env,
-                    verbose=1,
-                    n_steps=n_steps_per_env,
-                    batch_size=minibatch_size,
-                    learning_rate=learning_rate,
-                    device="cpu",
-                    policy_kwargs=policy_kwargs,
-                    ent_coef=0.005,  # Entropy coefficient for exploration
-                    # clip_range=0.13,  # PPO clipping parameter
-                    gae_lambda=0.95,  # GAE lambda
-                    gamma=0.99 , # Discount factor
-                    # target_kl=0.03,
-                    # n_epochs=5
-                )
-
-            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
-                                          eval_freq=n_steps_per_env * 10, save_freq=n_steps_per_env * 10,
-                                          backup_env_file=__file__)
-        except KeyboardInterrupt:
-            sleep(1)  # wait for child processes
-            print("\nctrl+c pressed, aborting...\n")
-            servers.kill()
-            return
-
-        env.close()
-        eval_env.close()
-        servers.kill()
-
-    def test(self, args):
-
-        # Uses different server and monitor ports
-        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        server = Train_Server(self.server_p - 1, self.monitor_p, 1)
-        env = WalkEnv(self.ip, self.server_p - 1)
-        model = PPO.load(args["model_file"], env=env)
-
-        try:
-            self.export_model(args["model_file"], args["model_file"] + ".pkl",
-                              False)  # Export to pkl to create custom behavior
-            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
-        except KeyboardInterrupt:
-            print()
-
-        env.close()
-        server.kill()
-
-
-if __name__ == "__main__":
-    from types import SimpleNamespace
-
-    # 创建默认参数
-    script_args = SimpleNamespace(
-        args=SimpleNamespace(
-            i='127.0.0.1',  # Server IP
-            p=3100,  # Server port
-            m=3200,  # Monitor port
-            r=0,  # Robot type
-            t='Gym',  # Team name
-            u=1  # Uniform number
-        )
-    )
-
-    trainer = Train(script_args)
-    trainer.train({})
-    # trainer.test({"model_file": "scripts/gyms/logs/Walk_R0_000/best_model.zip",
-                #   "folder_dir": "scripts/gyms/logs/Walk_R0_000/",})

scripts/gyms/logs/Walk_R0_002/Walk.py

@@ -1,625 +0,0 @@
-import os
-import numpy as np
-import math
-import time
-from time import sleep
-from random import random
-from random import uniform
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.vec_env import SubprocVecEnv
-
-import gymnasium as gym
-from gymnasium import spaces
-
-from scripts.commons.Train_Base import Train_Base
-from scripts.commons.Server import Server as Train_Server
-
-from agent.base_agent import Base_Agent
-from utils.math_ops import MathOps
-
-from scipy.spatial.transform import Rotation as R
-
-'''
-Objective:
-Learn how to run forward using step primitive
-----------
-- class Basic_Run: implements an OpenAI custom gym
-- class Train:  implements algorithms to train a new model or test an existing model
-'''
-
-
-class WalkEnv(gym.Env):
-    def __init__(self, ip, server_p) -> None:
-
-        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
-        self.Player = player = Base_Agent(
-            team_name="Gym",
-            number=1,
-            host=ip,
-            port=server_p
-        )
-        self.robot_type = self.Player.robot
-        self.step_counter = 0  # to limit episode size
-        self.force_play_on = True
-
-        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
-        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
-        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
-        self.isfallen = False
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.debug_every_n_steps = 5
-        self.enable_debug_joint_status = False
-        self.calibrate_nominal_from_neutral = True
-        self.auto_calibrate_train_sim_flip = True
-        self.nominal_calibrated_once = False
-        self.flip_calibrated_once = False
-        self._target_hz = 0.0
-        self._target_dt = 0.0
-        self._last_sync_time = None
-        target_hz_env = 0
-        if target_hz_env:
-            try:
-                self._target_hz = float(target_hz_env)
-            except ValueError:
-                self._target_hz = 0.0
-        if self._target_hz > 0.0:
-            self._target_dt = 1.0 / self._target_hz
-
-        # State space
-        # 原始观测大小: 78
-        obs_size = 78
-        self.obs = np.zeros(obs_size, np.float32)
-        self.observation_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(obs_size,),
-            dtype=np.float32
-        )
-
-        action_dim = len(self.Player.robot.ROBOT_MOTORS)
-        self.no_of_actions = action_dim
-        self.action_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(action_dim,),
-            dtype=np.float32
-        )
-
-        # 中立姿态
-        self.joint_nominal_position = np.array(
-            [
-                0.0,
-                0.0,
-                0.0,
-                1.4,
-                0.0,
-                -0.4,
-                0.0,
-                -1.4,
-                0.0,
-                0.4,
-                0.0,
-                -0.4,
-                0.0,
-                0.0,
-                0.8,
-                -0.4,
-                0.0,
-                0.4,
-                0.0,
-                0.0,
-                -0.8,
-                0.4,
-                0.0,
-            ]
-        )
-        self.joint_nominal_position = np.zeros(self.no_of_actions)
-        self.train_sim_flip = np.array(
-            [
-                1.0,  # 0: Head_yaw (he1)
-                -1.0,  # 1: Head_pitch (he2)
-                1.0,  # 2: Left_Shoulder_Pitch (lae1)
-                -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                -1.0,  # 4: Left_Elbow_Pitch (lae3)
-                1.0,  # 5: Left_Elbow_Yaw (lae4)
-                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                -1.0,  # 7: Right_Shoulder_Roll (rae2)
-                1.0,  # 8: Right_Elbow_Pitch (rae3)
-                1.0,  # 9: Right_Elbow_Yaw (rae4)
-                1.0,  # 10: Waist (te1)
-                1.0,  # 11: Left_Hip_Pitch (lle1)
-                -1.0,  # 12: Left_Hip_Roll (lle2)
-                -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,  # 14: Left_Knee_Pitch (lle4)
-                1.0,  # 15: Left_Ankle_Pitch (lle5)
-                -1.0,  # 16: Left_Ankle_Roll (lle6)
-                -1.0,  # 17: Right_Hip_Pitch (rle1)
-                -1.0,  # 18: Right_Hip_Roll (rle2)
-                -1.0,  # 19: Right_Hip_Yaw (rle3)
-                -1.0,  # 20: Right_Knee_Pitch (rle4)
-                -1.0,  # 21: Right_Ankle_Pitch (rle5)
-                -1.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-
-        self.scaling_factor = 0.1
-        # self.scaling_factor = 1
-
-        # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = False
-        self.reset_beam_yaw_range_deg = 180  # randomize target direction fully to encourage learning a real walk instead of a fixed gait
-        self.reset_joint_noise_rad = 0.035
-        self.reset_perturb_steps = 5
-        self.reset_recover_steps = 8
-
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Track previous position
-        self.Player.server.connect()
-        # sleep(2.0)  # Longer wait for connection to establish completely
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-        self.start_time = time.time()
-
-    def debug_log(self, message):
-        print(message)
-        try:
-            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
-            with open(log_path, "a", encoding="utf-8") as f:
-                f.write(message + "\n")
-        except OSError:
-            pass
-
-    def observe(self, init=False):
-
-        """获取当前观测值"""
-        robot = self.Player.robot
-        world = self.Player.world
-
-        # Safety check: ensure data is available
-
-        # 计算目标速度
-        raw_target = self.target_position - world.global_position[:2]
-        velocity = MathOps.rotate_2d_vec(
-            raw_target,
-            -robot.global_orientation_euler[2],
-            is_rad=False
-        )
-
-        # 计算相对方向
-        rel_orientation = MathOps.vector_angle(velocity) * 0.3
-        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
-
-        velocity = np.concatenate([velocity, np.array([rel_orientation])])
-        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
-        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
-
-        # 关节状态
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        qpos_qvel_previous_action = np.concatenate([
-            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
-            radian_joint_speeds / 110.0 * self.train_sim_flip,
-            self.previous_action / 10.0,
-        ])
-
-        # 角速度
-        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
-
-        # 投影的重力方向
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-
-        # 组合观测
-        observation = np.concatenate([
-            qpos_qvel_previous_action,
-            ang_vel,
-            velocity,
-            projected_gravity,
-        ])
-
-        observation = np.clip(observation, -10.0, 10.0)
-        return observation.astype(np.float32)
-
-    def sync(self):
-        ''' Run a single simulation step '''
-        self.Player.server.receive()
-        self.Player.world.update()
-        self.Player.robot.commit_motor_targets_pd()
-        self.Player.server.send()
-        if self._target_dt > 0.0:
-            now = time.time()
-            if self._last_sync_time is None:
-                self._last_sync_time = now
-                return
-            elapsed = now - self._last_sync_time
-            remaining = self._target_dt - elapsed
-            if remaining > 0.0:
-                time.sleep(remaining)
-                now = time.time()
-            self._last_sync_time = now
-
-    def debug_joint_status(self):
-        robot = self.Player.robot
-        actual_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        target_joint_positions = getattr(
-            self,
-            'target_joint_positions',
-            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
-        )
-        joint_error = actual_joint_positions - target_joint_positions
-        leg_slice = slice(11, None)
-
-        self.debug_log(
-            "[WalkDebug] "
-            f"step={self.step_counter} "
-            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
-            f"target_xy={np.round(self.target_position, 3).tolist()} "
-            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
-            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
-            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
-            f"fallen={self.Player.world.global_position[2] < 0.3}"
-        )
-        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
-
-    def reset(self, seed=None, options=None):
-        '''
-        Reset and stabilize the robot
-        Note: for some behaviors it would be better to reduce stabilization or add noise
-        '''
-        r = self.Player.robot
-        super().reset(seed=seed)
-        if seed is not None:
-            np.random.seed(seed)
-
-        length1 = 2  # randomize target distance
-        length2 = np.random.uniform(0.6, 1)  # randomize target distance
-        length3 = np.random.uniform(0.6, 1)  # randomize target distance
-        angle2 = np.random.uniform(-30, 30)  # randomize initial orientation
-        angle3 = np.random.uniform(-30, 30)  # randomize target direction
-
-        self.step_counter = 0
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
-        self.walk_cycle_step = 0
-
-        # 随机 beam 目标位置和朝向，增加训练多样性
-        beam_x = (random() - 0.5) * 10
-        beam_y = (random() - 0.5) * 10
-        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
-
-        for _ in range(5):
-            self.Player.server.receive()
-            self.Player.world.update()
-            self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
-            self.Player.server.send()
-
-        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
-        finished_count = 0
-        for _ in range(50):
-            finished = self.Player.skills_manager.execute("Neutral")
-            self.sync()
-            if finished:
-                finished_count += 1
-                if finished_count >= 20:  # 假设需要连续20次完成才算成功
-                    break
-
-        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-            # Perturb waist + lower body only (10:), keep head/arms stable.
-            perturb_action[10:] = np.random.uniform(
-                -self.reset_joint_noise_rad,
-                self.reset_joint_noise_rad,
-                size=(self.no_of_actions - 10,)
-            )
-
-            for _ in range(self.reset_perturb_steps):
-                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-            for i in range(self.reset_recover_steps):
-                # Linearly fade perturbation to help policy start from near-neutral.
-                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-        # memory variables
-        self.sync()  
-        self.initial_position = np.array(self.Player.world.global_position[:2])
-        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions, np.float32)
-        # Build target in the robot's current forward direction instead of fixed global +x.
-        heading_deg = float(r.global_orientation_euler[2])
-        forward_offset = MathOps.rotate_2d_vec(np.array([length1, 0.0]), heading_deg, is_rad=False)
-        point1 = self.initial_position + forward_offset
-        point2 = point1 + MathOps.rotate_2d_vec(np.array([length2, 0]), angle2, is_rad=False)
-        point3 = point2 + MathOps.rotate_2d_vec(np.array([length3, 0]), angle3, is_rad=False)
-        self.point_list = [point1]
-        self.target_position = self.point_list[self.waypoint_index]
-        self.initial_height = self.Player.world.global_position[2]
-
-        return self.observe(True), {}
-
-    def render(self, mode='human', close=False):
-        return
-
-    def compute_reward(self, previous_pos, current_pos, action):
-        height = float(self.Player.world.global_position[2])
-
-        orientation_quat_inv = R.from_quat(self.Player.robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-        tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        ang_vel = np.deg2rad(self.Player.robot.gyroscope)
-        ang_vel_mag = float(np.linalg.norm(ang_vel))
-
-        is_fallen = height < 0.3
-        if is_fallen:
-            remain = max(0, 800 - self.step_counter)
-            return -8.0 - 0.01 * remain
-        
-
-
-        # # 目标方向
-        # to_target = self.target_position - current_pos
-        # dist_to_target = float(np.linalg.norm(to_target))
-        # if dist_to_target < 0.5:
-        #     return 15.0
-
-        # forward_dir = to_target / dist_to_target if dist_to_target > 0.1 else np.array([1.0, 0.0])
-        # delta_pos = current_pos - previous_pos
-        # forward_step = float(np.dot(delta_pos, forward_dir))
-        # lateral_step = float(np.linalg.norm(delta_pos - forward_dir * forward_step))
-
-        # 奖励项
-        # progress_reward = 2 * forward_step
-        # lateral_penalty = -0.1 * lateral_step
-        alive_bonus = 0.3
-
-        # action_penalty = -0.01 * float(np.linalg.norm(action))
-        smoothness_penalty = -0.03 * float(np.linalg.norm(action - self.last_action_for_reward))
-
-        posture_penalty = -1.0 * (tilt_mag)
-        # ang_vel_penalty = -0.05 * ang_vel_mag
-
-        target_height = self.initial_height
-        height_error =  height - target_height if abs(height - target_height) > 0.05 else 0.0
-        height_penalty = -2.0 * abs(height_error)  # 惩罚高度偏离，系数可调
-
-        # # 在 compute_reward 开头附近，添加高度变化率计算
-        # if not hasattr(self, 'last_height'):
-        #     self.last_height = height
-        #     self.last_height_time = self.step_counter  # 可选，用于时间间隔
-        # height_rate = height - self.last_height  # 正为上升，负为下降
-        # self.last_height = height
-
-        # 惩罚高度下降（负变化率）
-        # height_down_penalty = -5.0 * max(0, -height_rate)  # 系数可调，-height_rate 为正表示下降幅度
-
-        # # 在 compute_reward 中
-        # if self.step_counter > 50:
-        #     avg_prev_action = np.mean(self.prev_action_history, axis=0)
-        #     novelty = float(np.linalg.norm(action - avg_prev_action))
-        #     exploration_bonus = 0.05 * novelty
-        # else:
-        #     exploration_bonus = 0
-
-        # self.prev_action_history[self.history_idx] = action
-        # self.history_idx = (self.history_idx + 1) % 50
-
-
-        total = (
-            # progress_reward  + 
-                 alive_bonus + 
-                 # lateral_penalty +
-                 # action_penalty +
-                smoothness_penalty +
-                 posture_penalty
-                #  + ang_vel_penalty
-                 + height_penalty
-                #  + exploration_bonus
-                # + height_down_penalty
-                 )
-        if time.time() - self.start_time >= 1200:
-            self.start_time = time.time()
-            print(
-                #   f"progress_reward:{progress_reward:.4f}",
-                #   f"lateral_penalty:{lateral_penalty:.4f}",
-                #   f"action_penalty:{action_penalty:.4f}"s, 
-                  f"height_penalty:{height_penalty:.4f}",
-                  f"smoothness_penalty:{smoothness_penalty:.4f},",
-                  f"posture_penalty:{posture_penalty:.4f}",
-                #   f"ang_vel_penalty:{ang_vel_penalty:.4f}",
-                #   f"height_down_penalty:{height_down_penalty:.4f}",
-                #   f"exploration_bonus:{exploration_bonus:.4f}"
-            )
-
-        return total
-
-
-
-    def step(self, action):
-
-        r = self.Player.robot
-        self.previous_action = action
-
-        self.target_joint_positions = (
-                # self.joint_nominal_position +
-                 self.scaling_factor * action
-        )
-        self.target_joint_positions *= self.train_sim_flip
-
-        for idx, target in enumerate(self.target_joint_positions):
-            r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-            )
-
-        self.previous_action = action
-
-        self.sync()  # run simulation step
-        self.step_counter += 1
-
-        if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
-            self.debug_joint_status()
-
-        current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
-
-        # Compute reward based on movement from previous step
-        reward = self.compute_reward(self.previous_pos, current_pos, action)
-
-        # Update previous position
-        self.previous_pos = current_pos.copy()
-        self.last_action_for_reward = action.copy()
-
-        # Fall detection and penalty
-        is_fallen = self.Player.world.global_position[2] < 0.3
-
-        # terminal state: the robot is falling or timeout
-        terminated = is_fallen or self.step_counter > 800 or self.route_completed
-        truncated = False
-
-        return self.observe(), reward, terminated, truncated, {}
-
-
-class Train(Train_Base):
-    def __init__(self, script) -> None:
-        super().__init__(script)
-
-    def train(self, args):
-
-        # --------------------------------------- Learning parameters
-        n_envs = 20  # Reduced from 8 to decrease CPU/network pressure during init
-        if n_envs < 1:
-            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
-        n_steps_per_env = 1024  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = 128  # should be a factor of (n_steps_per_env * n_envs)
-        total_steps = 30000000
-        learning_rate = 3e-4
-        folder_name = f'Walk_R{self.robot_type}'
-        model_path = f'./scripts/gyms/logs/{folder_name}/'
-
-        print(f"Model path: {model_path}")
-        print(f"Using {n_envs} parallel environments")
-
-        # --------------------------------------- Run algorithm
-        def init_env(i_env):
-            def thunk():
-                return WalkEnv(self.ip, self.server_p + i_env)
-
-            return thunk
-
-        server_log_dir = os.path.join(model_path, "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1)  # include 1 extra server for testing
-
-        # Wait for servers to start
-        print(f"Starting {n_envs + 1} rcssservermj servers...")
-        print("Servers started, creating environments...")
-
-        env = SubprocVecEnv([init_env(i) for i in range(n_envs)])
-        eval_env = SubprocVecEnv([init_env(n_envs)])
-
-        try:
-            # Custom policy network architecture
-            policy_kwargs = dict(
-                net_arch=dict(
-                    pi=[512, 256, 128],  # Policy network: 3 layers
-                    vf=[512, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
-            )
-
-            if "model_file" in args:  # retrain
-                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
-                                 batch_size=minibatch_size, learning_rate=learning_rate)
-            else:  # train new model
-                model = PPO(
-                    "MlpPolicy",
-                    env=env,
-                    verbose=1,
-                    n_steps=n_steps_per_env,
-                    batch_size=minibatch_size,
-                    learning_rate=learning_rate,
-                    device="cpu",
-                    policy_kwargs=policy_kwargs,
-                    ent_coef=0.005,  # Entropy coefficient for exploration
-                    # clip_range=0.13,  # PPO clipping parameter
-                    gae_lambda=0.95,  # GAE lambda
-                    gamma=0.99 , # Discount factor
-                    # target_kl=0.03,
-                    # n_epochs=5
-                )
-
-            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
-                                          eval_freq=n_steps_per_env * 10, save_freq=n_steps_per_env * 10,
-                                          backup_env_file=__file__)
-        except KeyboardInterrupt:
-            sleep(1)  # wait for child processes
-            print("\nctrl+c pressed, aborting...\n")
-            servers.kill()
-            return
-
-        env.close()
-        eval_env.close()
-        servers.kill()
-
-    def test(self, args):
-
-        # Uses different server and monitor ports
-        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        server = Train_Server(self.server_p - 1, self.monitor_p, 1)
-        env = WalkEnv(self.ip, self.server_p - 1)
-        model = PPO.load(args["model_file"], env=env)
-
-        try:
-            self.export_model(args["model_file"], args["model_file"] + ".pkl",
-                              False)  # Export to pkl to create custom behavior
-            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
-        except KeyboardInterrupt:
-            print()
-
-        env.close()
-        server.kill()
-
-
-if __name__ == "__main__":
-    from types import SimpleNamespace
-
-    # 创建默认参数
-    script_args = SimpleNamespace(
-        args=SimpleNamespace(
-            i='127.0.0.1',  # Server IP
-            p=3100,  # Server port
-            m=3200,  # Monitor port
-            r=0,  # Robot type
-            t='Gym',  # Team name
-            u=1  # Uniform number
-        )
-    )
-
-    trainer = Train(script_args)
-    trainer.train({})
-    # trainer.test({"model_file": "scripts/gyms/logs/Walk_R0_000/best_model.zip",
-                #   "folder_dir": "scripts/gyms/logs/Walk_R0_000/",})

scripts/gyms/logs/Walk_R0_003/Walk.py

@@ -1,625 +0,0 @@
-import os
-import numpy as np
-import math
-import time
-from time import sleep
-from random import random
-from random import uniform
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.vec_env import SubprocVecEnv
-
-import gymnasium as gym
-from gymnasium import spaces
-
-from scripts.commons.Train_Base import Train_Base
-from scripts.commons.Server import Server as Train_Server
-
-from agent.base_agent import Base_Agent
-from utils.math_ops import MathOps
-
-from scipy.spatial.transform import Rotation as R
-
-'''
-Objective:
-Learn how to run forward using step primitive
-----------
-- class Basic_Run: implements an OpenAI custom gym
-- class Train:  implements algorithms to train a new model or test an existing model
-'''
-
-
-class WalkEnv(gym.Env):
-    def __init__(self, ip, server_p) -> None:
-
-        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
-        self.Player = player = Base_Agent(
-            team_name="Gym",
-            number=1,
-            host=ip,
-            port=server_p
-        )
-        self.robot_type = self.Player.robot
-        self.step_counter = 0  # to limit episode size
-        self.force_play_on = True
-
-        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
-        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
-        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
-        self.isfallen = False
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.debug_every_n_steps = 5
-        self.enable_debug_joint_status = False
-        self.calibrate_nominal_from_neutral = True
-        self.auto_calibrate_train_sim_flip = True
-        self.nominal_calibrated_once = False
-        self.flip_calibrated_once = False
-        self._target_hz = 0.0
-        self._target_dt = 0.0
-        self._last_sync_time = None
-        target_hz_env = 0
-        if target_hz_env:
-            try:
-                self._target_hz = float(target_hz_env)
-            except ValueError:
-                self._target_hz = 0.0
-        if self._target_hz > 0.0:
-            self._target_dt = 1.0 / self._target_hz
-
-        # State space
-        # 原始观测大小: 78
-        obs_size = 78
-        self.obs = np.zeros(obs_size, np.float32)
-        self.observation_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(obs_size,),
-            dtype=np.float32
-        )
-
-        action_dim = len(self.Player.robot.ROBOT_MOTORS)
-        self.no_of_actions = action_dim
-        self.action_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(action_dim,),
-            dtype=np.float32
-        )
-
-        # 中立姿态
-        self.joint_nominal_position = np.array(
-            [
-                0.0,
-                0.0,
-                0.0,
-                1.4,
-                0.0,
-                -0.4,
-                0.0,
-                -1.4,
-                0.0,
-                0.4,
-                0.0,
-                -0.4,
-                0.0,
-                0.0,
-                0.8,
-                -0.4,
-                0.0,
-                0.4,
-                0.0,
-                0.0,
-                -0.8,
-                0.4,
-                0.0,
-            ]
-        )
-        self.joint_nominal_position = np.zeros(self.no_of_actions)
-        self.train_sim_flip = np.array(
-            [
-                1.0,  # 0: Head_yaw (he1)
-                -1.0,  # 1: Head_pitch (he2)
-                1.0,  # 2: Left_Shoulder_Pitch (lae1)
-                -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                -1.0,  # 4: Left_Elbow_Pitch (lae3)
-                1.0,  # 5: Left_Elbow_Yaw (lae4)
-                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                -1.0,  # 7: Right_Shoulder_Roll (rae2)
-                1.0,  # 8: Right_Elbow_Pitch (rae3)
-                1.0,  # 9: Right_Elbow_Yaw (rae4)
-                1.0,  # 10: Waist (te1)
-                1.0,  # 11: Left_Hip_Pitch (lle1)
-                -1.0,  # 12: Left_Hip_Roll (lle2)
-                -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,  # 14: Left_Knee_Pitch (lle4)
-                1.0,  # 15: Left_Ankle_Pitch (lle5)
-                -1.0,  # 16: Left_Ankle_Roll (lle6)
-                -1.0,  # 17: Right_Hip_Pitch (rle1)
-                -1.0,  # 18: Right_Hip_Roll (rle2)
-                -1.0,  # 19: Right_Hip_Yaw (rle3)
-                -1.0,  # 20: Right_Knee_Pitch (rle4)
-                -1.0,  # 21: Right_Ankle_Pitch (rle5)
-                -1.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-
-        self.scaling_factor = 0.3
-        # self.scaling_factor = 1
-
-        # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = True
-        self.reset_beam_yaw_range_deg = 180  # randomize target direction fully to encourage learning a real walk instead of a fixed gait
-        self.reset_joint_noise_rad = 0.035
-        self.reset_perturb_steps = 5
-        self.reset_recover_steps = 8
-
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Track previous position
-        self.Player.server.connect()
-        # sleep(2.0)  # Longer wait for connection to establish completely
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-        self.start_time = time.time()
-
-    def debug_log(self, message):
-        print(message)
-        try:
-            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
-            with open(log_path, "a", encoding="utf-8") as f:
-                f.write(message + "\n")
-        except OSError:
-            pass
-
-    def observe(self, init=False):
-
-        """获取当前观测值"""
-        robot = self.Player.robot
-        world = self.Player.world
-
-        # Safety check: ensure data is available
-
-        # 计算目标速度
-        raw_target = self.target_position - world.global_position[:2]
-        velocity = MathOps.rotate_2d_vec(
-            raw_target,
-            -robot.global_orientation_euler[2],
-            is_rad=False
-        )
-
-        # 计算相对方向
-        rel_orientation = MathOps.vector_angle(velocity) * 0.3
-        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
-
-        velocity = np.concatenate([velocity, np.array([rel_orientation])])
-        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
-        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
-
-        # 关节状态
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        qpos_qvel_previous_action = np.concatenate([
-            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
-            radian_joint_speeds / 110.0 * self.train_sim_flip,
-            self.previous_action / 10.0,
-        ])
-
-        # 角速度
-        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
-
-        # 投影的重力方向
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-
-        # 组合观测
-        observation = np.concatenate([
-            qpos_qvel_previous_action,
-            ang_vel,
-            velocity,
-            projected_gravity,
-        ])
-
-        observation = np.clip(observation, -10.0, 10.0)
-        return observation.astype(np.float32)
-
-    def sync(self):
-        ''' Run a single simulation step '''
-        self.Player.server.receive()
-        self.Player.world.update()
-        self.Player.robot.commit_motor_targets_pd()
-        self.Player.server.send()
-        if self._target_dt > 0.0:
-            now = time.time()
-            if self._last_sync_time is None:
-                self._last_sync_time = now
-                return
-            elapsed = now - self._last_sync_time
-            remaining = self._target_dt - elapsed
-            if remaining > 0.0:
-                time.sleep(remaining)
-                now = time.time()
-            self._last_sync_time = now
-
-    def debug_joint_status(self):
-        robot = self.Player.robot
-        actual_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        target_joint_positions = getattr(
-            self,
-            'target_joint_positions',
-            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
-        )
-        joint_error = actual_joint_positions - target_joint_positions
-        leg_slice = slice(11, None)
-
-        self.debug_log(
-            "[WalkDebug] "
-            f"step={self.step_counter} "
-            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
-            f"target_xy={np.round(self.target_position, 3).tolist()} "
-            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
-            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
-            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
-            f"fallen={self.Player.world.global_position[2] < 0.3}"
-        )
-        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
-
-    def reset(self, seed=None, options=None):
-        '''
-        Reset and stabilize the robot
-        Note: for some behaviors it would be better to reduce stabilization or add noise
-        '''
-        r = self.Player.robot
-        super().reset(seed=seed)
-        if seed is not None:
-            np.random.seed(seed)
-
-        length1 = 2  # randomize target distance
-        length2 = np.random.uniform(0.6, 1)  # randomize target distance
-        length3 = np.random.uniform(0.6, 1)  # randomize target distance
-        angle2 = np.random.uniform(-30, 30)  # randomize initial orientation
-        angle3 = np.random.uniform(-30, 30)  # randomize target direction
-
-        self.step_counter = 0
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
-        self.walk_cycle_step = 0
-
-        # 随机 beam 目标位置和朝向，增加训练多样性
-        beam_x = (random() - 0.5) * 10
-        beam_y = (random() - 0.5) * 10
-        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
-
-        for _ in range(5):
-            self.Player.server.receive()
-            self.Player.world.update()
-            self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
-            self.Player.server.send()
-
-        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
-        finished_count = 0
-        for _ in range(50):
-            finished = self.Player.skills_manager.execute("Neutral")
-            self.sync()
-            if finished:
-                finished_count += 1
-                if finished_count >= 20:  # 假设需要连续20次完成才算成功
-                    break
-
-        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-            # Perturb waist + lower body only (10:), keep head/arms stable.
-            perturb_action[10:] = np.random.uniform(
-                -self.reset_joint_noise_rad,
-                self.reset_joint_noise_rad,
-                size=(self.no_of_actions - 10,)
-            )
-
-            for _ in range(self.reset_perturb_steps):
-                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-            for i in range(self.reset_recover_steps):
-                # Linearly fade perturbation to help policy start from near-neutral.
-                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-        # memory variables
-        self.sync()  
-        self.initial_position = np.array(self.Player.world.global_position[:2])
-        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions, np.float32)
-        # Build target in the robot's current forward direction instead of fixed global +x.
-        heading_deg = float(r.global_orientation_euler[2])
-        forward_offset = MathOps.rotate_2d_vec(np.array([length1, 0.0]), heading_deg, is_rad=False)
-        point1 = self.initial_position + forward_offset
-        point2 = point1 + MathOps.rotate_2d_vec(np.array([length2, 0]), angle2, is_rad=False)
-        point3 = point2 + MathOps.rotate_2d_vec(np.array([length3, 0]), angle3, is_rad=False)
-        self.point_list = [point1]
-        self.target_position = self.point_list[self.waypoint_index]
-        self.initial_height = self.Player.world.global_position[2]
-
-        return self.observe(True), {}
-
-    def render(self, mode='human', close=False):
-        return
-
-    def compute_reward(self, previous_pos, current_pos, action):
-        height = float(self.Player.world.global_position[2])
-
-        orientation_quat_inv = R.from_quat(self.Player.robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-        tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        ang_vel = np.deg2rad(self.Player.robot.gyroscope)
-        ang_vel_mag = float(np.linalg.norm(ang_vel))
-
-        is_fallen = height < 0.3
-        if is_fallen:
-            remain = max(0, 800 - self.step_counter)
-            return -8.0 - 0.01 * remain
-        
-
-
-        # # 目标方向
-        # to_target = self.target_position - current_pos
-        # dist_to_target = float(np.linalg.norm(to_target))
-        # if dist_to_target < 0.5:
-        #     return 15.0
-
-        # forward_dir = to_target / dist_to_target if dist_to_target > 0.1 else np.array([1.0, 0.0])
-        # delta_pos = current_pos - previous_pos
-        # forward_step = float(np.dot(delta_pos, forward_dir))
-        # lateral_step = float(np.linalg.norm(delta_pos - forward_dir * forward_step))
-
-        # 奖励项
-        # progress_reward = 2 * forward_step
-        # lateral_penalty = -0.1 * lateral_step
-        alive_bonus = 0.3
-
-        # action_penalty = -0.01 * float(np.linalg.norm(action))
-        smoothness_penalty = -0.03 * float(np.linalg.norm(action - self.last_action_for_reward))
-
-        posture_penalty = -1.0 * (tilt_mag)
-        ang_vel_penalty = -0.05 * ang_vel_mag
-
-        target_height = self.initial_height
-        height_error =  height - target_height
-        height_penalty = -2.0 * abs(height_error)  # 惩罚高度偏离，系数可调
-
-        # # 在 compute_reward 开头附近，添加高度变化率计算
-        # if not hasattr(self, 'last_height'):
-        #     self.last_height = height
-        #     self.last_height_time = self.step_counter  # 可选，用于时间间隔
-        # height_rate = height - self.last_height  # 正为上升，负为下降
-        # self.last_height = height
-
-        # 惩罚高度下降（负变化率）
-        # height_down_penalty = -5.0 * max(0, -height_rate)  # 系数可调，-height_rate 为正表示下降幅度
-
-        # # 在 compute_reward 中
-        # if self.step_counter > 50:
-        #     avg_prev_action = np.mean(self.prev_action_history, axis=0)
-        #     novelty = float(np.linalg.norm(action - avg_prev_action))
-        #     exploration_bonus = 0.05 * novelty
-        # else:
-        #     exploration_bonus = 0
-
-        # self.prev_action_history[self.history_idx] = action
-        # self.history_idx = (self.history_idx + 1) % 50
-
-
-        total = (
-            # progress_reward  + 
-                 alive_bonus + 
-                 # lateral_penalty +
-                 # action_penalty +
-                smoothness_penalty +
-                 posture_penalty
-                 + ang_vel_penalty
-                 + height_penalty
-                #  + exploration_bonus
-                # + height_down_penalty
-                 )
-        if time.time() - self.start_time >= 1200:
-            self.start_time = time.time()
-            print(
-                #   f"progress_reward:{progress_reward:.4f}",
-                #   f"lateral_penalty:{lateral_penalty:.4f}",
-                #   f"action_penalty:{action_penalty:.4f}"s, 
-                  f"height_penalty:{height_penalty:.4f}",
-                  f"smoothness_penalty:{smoothness_penalty:.4f},",
-                  f"posture_penalty:{posture_penalty:.4f}",
-                #   f"ang_vel_penalty:{ang_vel_penalty:.4f}",
-                #   f"height_down_penalty:{height_down_penalty:.4f}",
-                #   f"exploration_bonus:{exploration_bonus:.4f}"
-            )
-
-        return total
-
-
-
-    def step(self, action):
-
-        r = self.Player.robot
-        self.previous_action = action
-
-        self.target_joint_positions = (
-                # self.joint_nominal_position +
-                 self.scaling_factor * action
-        )
-        self.target_joint_positions *= self.train_sim_flip
-
-        for idx, target in enumerate(self.target_joint_positions):
-            r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-            )
-
-        self.previous_action = action
-
-        self.sync()  # run simulation step
-        self.step_counter += 1
-
-        if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
-            self.debug_joint_status()
-
-        current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
-
-        # Compute reward based on movement from previous step
-        reward = self.compute_reward(self.previous_pos, current_pos, action)
-
-        # Update previous position
-        self.previous_pos = current_pos.copy()
-        self.last_action_for_reward = action.copy()
-
-        # Fall detection and penalty
-        is_fallen = self.Player.world.global_position[2] < 0.3
-
-        # terminal state: the robot is falling or timeout
-        terminated = is_fallen or self.step_counter > 800 or self.route_completed
-        truncated = False
-
-        return self.observe(), reward, terminated, truncated, {}
-
-
-class Train(Train_Base):
-    def __init__(self, script) -> None:
-        super().__init__(script)
-
-    def train(self, args):
-
-        # --------------------------------------- Learning parameters
-        n_envs = 20  # Reduced from 8 to decrease CPU/network pressure during init
-        if n_envs < 1:
-            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
-        n_steps_per_env = 1024  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = 128  # should be a factor of (n_steps_per_env * n_envs)
-        total_steps = 30000000
-        learning_rate = 1e-4
-        folder_name = f'Walk_R{self.robot_type}'
-        model_path = f'./scripts/gyms/logs/{folder_name}/'
-
-        print(f"Model path: {model_path}")
-        print(f"Using {n_envs} parallel environments")
-
-        # --------------------------------------- Run algorithm
-        def init_env(i_env):
-            def thunk():
-                return WalkEnv(self.ip, self.server_p + i_env)
-
-            return thunk
-
-        server_log_dir = os.path.join(model_path, "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1)  # include 1 extra server for testing
-
-        # Wait for servers to start
-        print(f"Starting {n_envs + 1} rcssservermj servers...")
-        print("Servers started, creating environments...")
-
-        env = SubprocVecEnv([init_env(i) for i in range(n_envs)])
-        eval_env = SubprocVecEnv([init_env(n_envs)])
-
-        try:
-            # Custom policy network architecture
-            policy_kwargs = dict(
-                net_arch=dict(
-                    pi=[512, 256, 128],  # Policy network: 3 layers
-                    vf=[512, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
-            )
-
-            if "model_file" in args:  # retrain
-                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
-                                 batch_size=minibatch_size, learning_rate=learning_rate)
-            else:  # train new model
-                model = PPO(
-                    "MlpPolicy",
-                    env=env,
-                    verbose=1,
-                    n_steps=n_steps_per_env,
-                    batch_size=minibatch_size,
-                    learning_rate=learning_rate,
-                    device="cpu",
-                    policy_kwargs=policy_kwargs,
-                    ent_coef=0.001,  # Entropy coefficient for exploration
-                    # clip_range=0.13,  # PPO clipping parameter
-                    gae_lambda=0.95,  # GAE lambda
-                    gamma=0.99 , # Discount factor
-                    target_kl=0.03,
-                    # n_epochs=5
-                )
-
-            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
-                                          eval_freq=n_steps_per_env * 10, save_freq=n_steps_per_env * 10,
-                                          backup_env_file=__file__)
-        except KeyboardInterrupt:
-            sleep(1)  # wait for child processes
-            print("\nctrl+c pressed, aborting...\n")
-            servers.kill()
-            return
-
-        env.close()
-        eval_env.close()
-        servers.kill()
-
-    def test(self, args):
-
-        # Uses different server and monitor ports
-        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        server = Train_Server(self.server_p - 1, self.monitor_p, 1)
-        env = WalkEnv(self.ip, self.server_p - 1)
-        model = PPO.load(args["model_file"], env=env)
-
-        try:
-            self.export_model(args["model_file"], args["model_file"] + ".pkl",
-                              False)  # Export to pkl to create custom behavior
-            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
-        except KeyboardInterrupt:
-            print()
-
-        env.close()
-        server.kill()
-
-
-if __name__ == "__main__":
-    from types import SimpleNamespace
-
-    # 创建默认参数
-    script_args = SimpleNamespace(
-        args=SimpleNamespace(
-            i='127.0.0.1',  # Server IP
-            p=3100,  # Server port
-            m=3200,  # Monitor port
-            r=0,  # Robot type
-            t='Gym',  # Team name
-            u=1  # Uniform number
-        )
-    )
-
-    trainer = Train(script_args)
-    trainer.train({})
-    # trainer.test({"model_file": "scripts/gyms/logs/Walk_R0_000/best_model.zip",
-                #   "folder_dir": "scripts/gyms/logs/Walk_R0_000/",})

scripts/gyms/logs/Walk_R0_004/Walk.py

@@ -1,626 +0,0 @@
-import os
-import numpy as np
-import math
-import time
-from time import sleep
-from random import random
-from random import uniform
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.vec_env import SubprocVecEnv
-
-import gymnasium as gym
-from gymnasium import spaces
-
-from scripts.commons.Train_Base import Train_Base
-from scripts.commons.Server import Server as Train_Server
-
-from agent.base_agent import Base_Agent
-from utils.math_ops import MathOps
-
-from scipy.spatial.transform import Rotation as R
-
-'''
-Objective:
-Learn how to run forward using step primitive
-----------
-- class Basic_Run: implements an OpenAI custom gym
-- class Train:  implements algorithms to train a new model or test an existing model
-'''
-
-
-class WalkEnv(gym.Env):
-    def __init__(self, ip, server_p) -> None:
-
-        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
-        self.Player = player = Base_Agent(
-            team_name="Gym",
-            number=1,
-            host=ip,
-            port=server_p
-        )
-        self.robot_type = self.Player.robot
-        self.step_counter = 0  # to limit episode size
-        self.force_play_on = True
-
-        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
-        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
-        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
-        self.isfallen = False
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.debug_every_n_steps = 5
-        self.enable_debug_joint_status = False
-        self.calibrate_nominal_from_neutral = True
-        self.auto_calibrate_train_sim_flip = True
-        self.nominal_calibrated_once = False
-        self.flip_calibrated_once = False
-        self._target_hz = 0.0
-        self._target_dt = 0.0
-        self._last_sync_time = None
-        target_hz_env = 0
-        if target_hz_env:
-            try:
-                self._target_hz = float(target_hz_env)
-            except ValueError:
-                self._target_hz = 0.0
-        if self._target_hz > 0.0:
-            self._target_dt = 1.0 / self._target_hz
-
-        # State space
-        # 原始观测大小: 78
-        obs_size = 78
-        self.obs = np.zeros(obs_size, np.float32)
-        self.observation_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(obs_size,),
-            dtype=np.float32
-        )
-
-        action_dim = len(self.Player.robot.ROBOT_MOTORS)
-        self.no_of_actions = action_dim
-        self.action_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(action_dim,),
-            dtype=np.float32
-        )
-
-        # 中立姿态
-        self.joint_nominal_position = np.array(
-            [
-                0.0,
-                0.0,
-                0.0,
-                1.4,
-                0.0,
-                -0.4,
-                0.0,
-                -1.4,
-                0.0,
-                0.4,
-                0.0,
-                -0.4,
-                0.0,
-                0.0,
-                0.8,
-                -0.4,
-                0.0,
-                0.4,
-                0.0,
-                0.0,
-                -0.8,
-                0.4,
-                0.0,
-            ]
-        )
-        self.joint_nominal_position = np.zeros(self.no_of_actions)
-        self.train_sim_flip = np.array(
-            [
-                1.0,  # 0: Head_yaw (he1)
-                -1.0,  # 1: Head_pitch (he2)
-                1.0,  # 2: Left_Shoulder_Pitch (lae1)
-                -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                -1.0,  # 4: Left_Elbow_Pitch (lae3)
-                1.0,  # 5: Left_Elbow_Yaw (lae4)
-                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                -1.0,  # 7: Right_Shoulder_Roll (rae2)
-                1.0,  # 8: Right_Elbow_Pitch (rae3)
-                1.0,  # 9: Right_Elbow_Yaw (rae4)
-                1.0,  # 10: Waist (te1)
-                1.0,  # 11: Left_Hip_Pitch (lle1)
-                -1.0,  # 12: Left_Hip_Roll (lle2)
-                -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,  # 14: Left_Knee_Pitch (lle4)
-                1.0,  # 15: Left_Ankle_Pitch (lle5)
-                -1.0,  # 16: Left_Ankle_Roll (lle6)
-                -1.0,  # 17: Right_Hip_Pitch (rle1)
-                -1.0,  # 18: Right_Hip_Roll (rle2)
-                -1.0,  # 19: Right_Hip_Yaw (rle3)
-                -1.0,  # 20: Right_Knee_Pitch (rle4)
-                -1.0,  # 21: Right_Ankle_Pitch (rle5)
-                -1.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-
-        self.scaling_factor = 0.3
-        # self.scaling_factor = 1
-
-        # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = True
-        self.reset_beam_yaw_range_deg = 180  # randomize target direction fully to encourage learning a real walk instead of a fixed gait
-        self.reset_joint_noise_rad = 0.015
-        self.reset_perturb_steps = 3
-        self.reset_recover_steps = 8
-
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Track previous position
-        self.Player.server.connect()
-        # sleep(2.0)  # Longer wait for connection to establish completely
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-        self.start_time = time.time()
-
-    def debug_log(self, message):
-        print(message)
-        try:
-            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
-            with open(log_path, "a", encoding="utf-8") as f:
-                f.write(message + "\n")
-        except OSError:
-            pass
-
-    def observe(self, init=False):
-
-        """获取当前观测值"""
-        robot = self.Player.robot
-        world = self.Player.world
-
-        # Safety check: ensure data is available
-
-        # 计算目标速度
-        raw_target = self.target_position - world.global_position[:2]
-        velocity = MathOps.rotate_2d_vec(
-            raw_target,
-            -robot.global_orientation_euler[2],
-            is_rad=False
-        )
-
-        # 计算相对方向
-        rel_orientation = MathOps.vector_angle(velocity) * 0.3
-        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
-
-        velocity = np.concatenate([velocity, np.array([rel_orientation])])
-        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
-        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
-
-        # 关节状态
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        qpos_qvel_previous_action = np.concatenate([
-            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
-            radian_joint_speeds / 110.0 * self.train_sim_flip,
-            self.previous_action / 10.0,
-        ])
-
-        # 角速度
-        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
-
-        # 投影的重力方向
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-
-        # 组合观测
-        observation = np.concatenate([
-            qpos_qvel_previous_action,
-            ang_vel,
-            velocity,
-            projected_gravity,
-        ])
-
-        observation = np.clip(observation, -10.0, 10.0)
-        return observation.astype(np.float32)
-
-    def sync(self):
-        ''' Run a single simulation step '''
-        self.Player.server.receive()
-        self.Player.world.update()
-        self.Player.robot.commit_motor_targets_pd()
-        self.Player.server.send()
-        if self._target_dt > 0.0:
-            now = time.time()
-            if self._last_sync_time is None:
-                self._last_sync_time = now
-                return
-            elapsed = now - self._last_sync_time
-            remaining = self._target_dt - elapsed
-            if remaining > 0.0:
-                time.sleep(remaining)
-                now = time.time()
-            self._last_sync_time = now
-
-    def debug_joint_status(self):
-        robot = self.Player.robot
-        actual_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        target_joint_positions = getattr(
-            self,
-            'target_joint_positions',
-            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
-        )
-        joint_error = actual_joint_positions - target_joint_positions
-        leg_slice = slice(11, None)
-
-        self.debug_log(
-            "[WalkDebug] "
-            f"step={self.step_counter} "
-            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
-            f"target_xy={np.round(self.target_position, 3).tolist()} "
-            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
-            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
-            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
-            f"fallen={self.Player.world.global_position[2] < 0.3}"
-        )
-        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
-
-    def reset(self, seed=None, options=None):
-        '''
-        Reset and stabilize the robot
-        Note: for some behaviors it would be better to reduce stabilization or add noise
-        '''
-        r = self.Player.robot
-        super().reset(seed=seed)
-        if seed is not None:
-            np.random.seed(seed)
-
-        length1 = 2  # randomize target distance
-        length2 = np.random.uniform(0.6, 1)  # randomize target distance
-        length3 = np.random.uniform(0.6, 1)  # randomize target distance
-        angle2 = np.random.uniform(-30, 30)  # randomize initial orientation
-        angle3 = np.random.uniform(-30, 30)  # randomize target direction
-
-        self.step_counter = 0
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
-        self.walk_cycle_step = 0
-
-        # 随机 beam 目标位置和朝向，增加训练多样性
-        beam_x = (random() - 0.5) * 10
-        beam_y = (random() - 0.5) * 10
-        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
-
-        for _ in range(5):
-            self.Player.server.receive()
-            self.Player.world.update()
-            self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
-            self.Player.server.send()
-
-        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
-        finished_count = 0
-        for _ in range(50):
-            finished = self.Player.skills_manager.execute("Neutral")
-            self.sync()
-            if finished:
-                finished_count += 1
-                if finished_count >= 20:  # 假设需要连续20次完成才算成功
-                    break
-
-        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-            # Perturb waist + lower body only (10:), keep head/arms stable.
-            perturb_action[10:] = np.random.uniform(
-                -self.reset_joint_noise_rad,
-                self.reset_joint_noise_rad,
-                size=(self.no_of_actions - 10,)
-            )
-
-            for _ in range(self.reset_perturb_steps):
-                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-            for i in range(self.reset_recover_steps):
-                # Linearly fade perturbation to help policy start from near-neutral.
-                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-        # memory variables
-        self.sync()  
-        self.initial_position = np.array(self.Player.world.global_position[:2])
-        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions, np.float32)
-        # Build target in the robot's current forward direction instead of fixed global +x.
-        heading_deg = float(r.global_orientation_euler[2])
-        forward_offset = MathOps.rotate_2d_vec(np.array([length1, 0.0]), heading_deg, is_rad=False)
-        point1 = self.initial_position + forward_offset
-        point2 = point1 + MathOps.rotate_2d_vec(np.array([length2, 0]), angle2, is_rad=False)
-        point3 = point2 + MathOps.rotate_2d_vec(np.array([length3, 0]), angle3, is_rad=False)
-        self.point_list = [point1]
-        self.target_position = self.point_list[self.waypoint_index]
-        self.initial_height = self.Player.world.global_position[2]
-
-        return self.observe(True), {}
-
-    def render(self, mode='human', close=False):
-        return
-
-    def compute_reward(self, previous_pos, current_pos, action):
-        height = float(self.Player.world.global_position[2])
-
-        orientation_quat_inv = R.from_quat(self.Player.robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-        tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        ang_vel = np.deg2rad(self.Player.robot.gyroscope)
-        ang_vel_mag = float(np.linalg.norm(ang_vel))
-
-        is_fallen = height < 0.3
-        if is_fallen:
-            # remain = max(0, 800 - self.step_counter)
-            # return -8.0 - 0.01 * remain
-            return -1.0
-        
-
-
-        # # 目标方向
-        # to_target = self.target_position - current_pos
-        # dist_to_target = float(np.linalg.norm(to_target))
-        # if dist_to_target < 0.5:
-        #     return 15.0
-
-        # forward_dir = to_target / dist_to_target if dist_to_target > 0.1 else np.array([1.0, 0.0])
-        # delta_pos = current_pos - previous_pos
-        # forward_step = float(np.dot(delta_pos, forward_dir))
-        # lateral_step = float(np.linalg.norm(delta_pos - forward_dir * forward_step))
-
-        # 奖励项
-        # progress_reward = 2 * forward_step
-        # lateral_penalty = -0.1 * lateral_step
-        alive_bonus = 2.0
-
-        # action_penalty = -0.01 * float(np.linalg.norm(action))
-        smoothness_penalty = -0.01 * float(np.linalg.norm(action - self.last_action_for_reward))
-
-        posture_penalty = -0.3 * (tilt_mag)
-        ang_vel_penalty = -0.02 * ang_vel_mag
-
-        target_height = self.initial_height
-        height_error =  height - target_height
-        height_penalty = -0.5 * abs(height_error)  # 惩罚高度偏离，系数可调
-
-        # # 在 compute_reward 开头附近，添加高度变化率计算
-        # if not hasattr(self, 'last_height'):
-        #     self.last_height = height
-        #     self.last_height_time = self.step_counter  # 可选，用于时间间隔
-        # height_rate = height - self.last_height  # 正为上升，负为下降
-        # self.last_height = height
-
-        # 惩罚高度下降（负变化率）
-        # height_down_penalty = -5.0 * max(0, -height_rate)  # 系数可调，-height_rate 为正表示下降幅度
-
-        # # 在 compute_reward 中
-        # if self.step_counter > 50:
-        #     avg_prev_action = np.mean(self.prev_action_history, axis=0)
-        #     novelty = float(np.linalg.norm(action - avg_prev_action))
-        #     exploration_bonus = 0.05 * novelty
-        # else:
-        #     exploration_bonus = 0
-
-        # self.prev_action_history[self.history_idx] = action
-        # self.history_idx = (self.history_idx + 1) % 50
-
-
-        total = (
-            # progress_reward  + 
-                 alive_bonus + 
-                 # lateral_penalty +
-                 # action_penalty +
-                smoothness_penalty +
-                 posture_penalty
-                 + ang_vel_penalty
-                 + height_penalty
-                #  + exploration_bonus
-                # + height_down_penalty
-                 )
-        if time.time() - self.start_time >= 1200:
-            self.start_time = time.time()
-            print(
-                #   f"progress_reward:{progress_reward:.4f}",
-                #   f"lateral_penalty:{lateral_penalty:.4f}",
-                #   f"action_penalty:{action_penalty:.4f}"s, 
-                  f"height_penalty:{height_penalty:.4f}",
-                  f"smoothness_penalty:{smoothness_penalty:.4f},",
-                  f"posture_penalty:{posture_penalty:.4f}",
-                #   f"ang_vel_penalty:{ang_vel_penalty:.4f}",
-                #   f"height_down_penalty:{height_down_penalty:.4f}",
-                #   f"exploration_bonus:{exploration_bonus:.4f}"
-            )
-
-        return total
-
-
-
-    def step(self, action):
-
-        r = self.Player.robot
-        self.previous_action = action
-
-        self.target_joint_positions = (
-                # self.joint_nominal_position +
-                 self.scaling_factor * action
-        )
-        self.target_joint_positions *= self.train_sim_flip
-
-        for idx, target in enumerate(self.target_joint_positions):
-            r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=40, kd=1.0
-            )
-
-        self.previous_action = action
-
-        self.sync()  # run simulation step
-        self.step_counter += 1
-
-        if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
-            self.debug_joint_status()
-
-        current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
-
-        # Compute reward based on movement from previous step
-        reward = self.compute_reward(self.previous_pos, current_pos, action)
-
-        # Update previous position
-        self.previous_pos = current_pos.copy()
-        self.last_action_for_reward = action.copy()
-
-        # Fall detection and penalty
-        is_fallen = self.Player.world.global_position[2] < 0.3
-
-        # terminal state: the robot is falling or timeout
-        terminated = is_fallen or self.step_counter > 800 or self.route_completed
-        truncated = False
-
-        return self.observe(), reward, terminated, truncated, {}
-
-
-class Train(Train_Base):
-    def __init__(self, script) -> None:
-        super().__init__(script)
-
-    def train(self, args):
-
-        # --------------------------------------- Learning parameters
-        n_envs = 20  # Reduced from 8 to decrease CPU/network pressure during init
-        if n_envs < 1:
-            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
-        n_steps_per_env = 256  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = 512  # should be a factor of (n_steps_per_env * n_envs)
-        total_steps = 30000000
-        learning_rate = 3e-4
-        folder_name = f'Walk_R{self.robot_type}'
-        model_path = f'./scripts/gyms/logs/{folder_name}/'
-
-        print(f"Model path: {model_path}")
-        print(f"Using {n_envs} parallel environments")
-
-        # --------------------------------------- Run algorithm
-        def init_env(i_env):
-            def thunk():
-                return WalkEnv(self.ip, self.server_p + i_env)
-
-            return thunk
-
-        server_log_dir = os.path.join(model_path, "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1)  # include 1 extra server for testing
-
-        # Wait for servers to start
-        print(f"Starting {n_envs + 1} rcssservermj servers...")
-        print("Servers started, creating environments...")
-
-        env = SubprocVecEnv([init_env(i) for i in range(n_envs)])
-        eval_env = SubprocVecEnv([init_env(n_envs)])
-
-        try:
-            # Custom policy network architecture
-            policy_kwargs = dict(
-                net_arch=dict(
-                    pi=[512, 256, 128],  # Policy network: 3 layers
-                    vf=[512, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
-            )
-
-            if "model_file" in args:  # retrain
-                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
-                                 batch_size=minibatch_size, learning_rate=learning_rate)
-            else:  # train new model
-                model = PPO(
-                    "MlpPolicy",
-                    env=env,
-                    verbose=1,
-                    n_steps=n_steps_per_env,
-                    batch_size=minibatch_size,
-                    learning_rate=learning_rate,
-                    device="cpu",
-                    policy_kwargs=policy_kwargs,
-                    ent_coef=0.05,  # Entropy coefficient for exploration
-                    # clip_range=0.13,  # PPO clipping parameter
-                    gae_lambda=0.95,  # GAE lambda
-                    gamma=0.95 , # Discount factor
-                    target_kl=0.03,
-                    n_epochs=5
-                )
-
-            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
-                                          eval_freq=n_steps_per_env * 10, save_freq=n_steps_per_env * 10,
-                                          backup_env_file=__file__)
-        except KeyboardInterrupt:
-            sleep(1)  # wait for child processes
-            print("\nctrl+c pressed, aborting...\n")
-            servers.kill()
-            return
-
-        env.close()
-        eval_env.close()
-        servers.kill()
-
-    def test(self, args):
-
-        # Uses different server and monitor ports
-        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        server = Train_Server(self.server_p - 1, self.monitor_p, 1)
-        env = WalkEnv(self.ip, self.server_p - 1)
-        model = PPO.load(args["model_file"], env=env)
-
-        try:
-            self.export_model(args["model_file"], args["model_file"] + ".pkl",
-                              False)  # Export to pkl to create custom behavior
-            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
-        except KeyboardInterrupt:
-            print()
-
-        env.close()
-        server.kill()
-
-
-if __name__ == "__main__":
-    from types import SimpleNamespace
-
-    # 创建默认参数
-    script_args = SimpleNamespace(
-        args=SimpleNamespace(
-            i='127.0.0.1',  # Server IP
-            p=3100,  # Server port
-            m=3200,  # Monitor port
-            r=0,  # Robot type
-            t='Gym',  # Team name
-            u=1  # Uniform number
-        )
-    )
-
-    trainer = Train(script_args)
-    trainer.train({})
-    # trainer.test({"model_file": "scripts/gyms/logs/Walk_R0_000/best_model.zip",
-                #   "folder_dir": "scripts/gyms/logs/Walk_R0_000/",})

scripts/gyms/logs/Walk_R0_005/Walk.py

@@ -1,660 +0,0 @@
-import os
-import numpy as np
-import math
-import time
-from time import sleep
-from random import random
-from random import uniform
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.monitor import Monitor
-from stable_baselines3.common.vec_env import SubprocVecEnv, DummyVecEnv
-
-import gymnasium as gym
-from gymnasium import spaces
-
-from scripts.commons.Train_Base import Train_Base
-from scripts.commons.Server import Server as Train_Server
-
-from agent.base_agent import Base_Agent
-from utils.math_ops import MathOps
-
-from scipy.spatial.transform import Rotation as R
-
-'''
-Objective:
-Learn how to run forward using step primitive
-----------
-- class Basic_Run: implements an OpenAI custom gym
-- class Train:  implements algorithms to train a new model or test an existing model
-'''
-
-
-class WalkEnv(gym.Env):
-    def __init__(self, ip, server_p) -> None:
-
-        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
-        self.Player = player = Base_Agent(
-            team_name="Gym",
-            number=1,
-            host=ip,
-            port=server_p
-        )
-        self.robot_type = self.Player.robot
-        self.step_counter = 0  # to limit episode size
-        self.force_play_on = True
-
-        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
-        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
-        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
-        self.isfallen = False
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.debug_every_n_steps = 5
-        self.enable_debug_joint_status = False
-        self.calibrate_nominal_from_neutral = True
-        self.auto_calibrate_train_sim_flip = True
-        self.nominal_calibrated_once = False
-        self.flip_calibrated_once = False
-        self._target_hz = 0.0
-        self._target_dt = 0.0
-        self._last_sync_time = None
-        target_hz_env = 0
-        if target_hz_env:
-            try:
-                self._target_hz = float(target_hz_env)
-            except ValueError:
-                self._target_hz = 0.0
-        if self._target_hz > 0.0:
-            self._target_dt = 1.0 / self._target_hz
-
-        # State space
-        # 原始观测大小: 78
-        obs_size = 78
-        self.obs = np.zeros(obs_size, np.float32)
-        self.observation_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(obs_size,),
-            dtype=np.float32
-        )
-
-        action_dim = len(self.Player.robot.ROBOT_MOTORS)
-        self.no_of_actions = action_dim
-        self.action_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(action_dim,),
-            dtype=np.float32
-        )
-
-        # 中立姿态
-        self.joint_nominal_position = np.array(
-            [
-                0.0,
-                0.0,
-                0.0,
-                1.4,
-                0.0,
-                -0.4,
-                0.0,
-                -1.4,
-                0.0,
-                0.4,
-                0.0,
-                -0.4,
-                0.0,
-                0.0,
-                0.8,
-                -0.4,
-                0.0,
-                0.4,
-                0.0,
-                0.0,
-                -0.8,
-                0.4,
-                0.0,
-            ]
-        )
-        self.joint_nominal_position = np.zeros(self.no_of_actions)
-        self.train_sim_flip = np.array(
-            [
-                1.0,  # 0: Head_yaw (he1)
-                -1.0,  # 1: Head_pitch (he2)
-                1.0,  # 2: Left_Shoulder_Pitch (lae1)
-                -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                -1.0,  # 4: Left_Elbow_Pitch (lae3)
-                1.0,  # 5: Left_Elbow_Yaw (lae4)
-                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                -1.0,  # 7: Right_Shoulder_Roll (rae2)
-                1.0,  # 8: Right_Elbow_Pitch (rae3)
-                1.0,  # 9: Right_Elbow_Yaw (rae4)
-                1.0,  # 10: Waist (te1)
-                1.0,  # 11: Left_Hip_Pitch (lle1)
-                -1.0,  # 12: Left_Hip_Roll (lle2)
-                -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,  # 14: Left_Knee_Pitch (lle4)
-                1.0,  # 15: Left_Ankle_Pitch (lle5)
-                -1.0,  # 16: Left_Ankle_Roll (lle6)
-                -1.0,  # 17: Right_Hip_Pitch (rle1)
-                -1.0,  # 18: Right_Hip_Roll (rle2)
-                -1.0,  # 19: Right_Hip_Yaw (rle3)
-                -1.0,  # 20: Right_Knee_Pitch (rle4)
-                -1.0,  # 21: Right_Ankle_Pitch (rle5)
-                -1.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-
-        self.scaling_factor = 0.3
-        # self.scaling_factor = 1
-
-        # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = True
-        self.reset_beam_yaw_range_deg = 180  # randomize target direction fully to encourage learning a real walk instead of a fixed gait
-        self.reset_joint_noise_rad = 0.015
-        self.reset_perturb_steps = 3
-        self.reset_recover_steps = 8
-
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Track previous position
-        self.Player.server.connect()
-        # sleep(2.0)  # Longer wait for connection to establish completely
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-        self.start_time = time.time()
-
-    def _reconnect_server(self):
-        try:
-            self.Player.server.shutdown()
-        except Exception:
-            pass
-
-        self.Player.server.connect()
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-
-    def _safe_receive_world_update(self, retries=1):
-        last_exc = None
-        for attempt in range(retries + 1):
-            try:
-                self.Player.server.receive()
-                self.Player.world.update()
-                return
-            except (ConnectionResetError, OSError) as exc:
-                last_exc = exc
-                if attempt >= retries:
-                    raise
-                self._reconnect_server()
-        if last_exc is not None:
-            raise last_exc
-
-    def debug_log(self, message):
-        print(message)
-        try:
-            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
-            with open(log_path, "a", encoding="utf-8") as f:
-                f.write(message + "\n")
-        except OSError:
-            pass
-
-    def observe(self, init=False):
-
-        """获取当前观测值"""
-        robot = self.Player.robot
-        world = self.Player.world
-
-        # Safety check: ensure data is available
-
-        # 计算目标速度
-        raw_target = self.target_position - world.global_position[:2]
-        velocity = MathOps.rotate_2d_vec(
-            raw_target,
-            -robot.global_orientation_euler[2],
-            is_rad=False
-        )
-
-        # 计算相对方向
-        rel_orientation = MathOps.vector_angle(velocity) * 0.3
-        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
-
-        velocity = np.concatenate([velocity, np.array([rel_orientation])])
-        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
-        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
-
-        # 关节状态
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        qpos_qvel_previous_action = np.concatenate([
-            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
-            radian_joint_speeds / 110.0 * self.train_sim_flip,
-            self.previous_action / 10.0,
-        ])
-
-        # 角速度
-        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
-
-        # 投影的重力方向
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-
-        # 组合观测
-        observation = np.concatenate([
-            qpos_qvel_previous_action,
-            ang_vel,
-            velocity,
-            projected_gravity,
-        ])
-
-        observation = np.clip(observation, -10.0, 10.0)
-        return observation.astype(np.float32)
-
-    def sync(self):
-        ''' Run a single simulation step '''
-        self._safe_receive_world_update(retries=1)
-        self.Player.robot.commit_motor_targets_pd()
-        self.Player.server.send()
-        if self._target_dt > 0.0:
-            now = time.time()
-            if self._last_sync_time is None:
-                self._last_sync_time = now
-                return
-            elapsed = now - self._last_sync_time
-            remaining = self._target_dt - elapsed
-            if remaining > 0.0:
-                time.sleep(remaining)
-                now = time.time()
-            self._last_sync_time = now
-
-    def debug_joint_status(self):
-        robot = self.Player.robot
-        actual_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        target_joint_positions = getattr(
-            self,
-            'target_joint_positions',
-            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
-        )
-        joint_error = actual_joint_positions - target_joint_positions
-        leg_slice = slice(11, None)
-
-        self.debug_log(
-            "[WalkDebug] "
-            f"step={self.step_counter} "
-            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
-            f"target_xy={np.round(self.target_position, 3).tolist()} "
-            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
-            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
-            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
-            f"fallen={self.Player.world.global_position[2] < 0.3}"
-        )
-        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
-
-    def reset(self, seed=None, options=None):
-        '''
-        Reset and stabilize the robot
-        Note: for some behaviors it would be better to reduce stabilization or add noise
-        '''
-        r = self.Player.robot
-        super().reset(seed=seed)
-        if seed is not None:
-            np.random.seed(seed)
-
-        length1 = 2  # randomize target distance
-        length2 = np.random.uniform(0.6, 1)  # randomize target distance
-        length3 = np.random.uniform(0.6, 1)  # randomize target distance
-        angle2 = np.random.uniform(-30, 30)  # randomize initial orientation
-        angle3 = np.random.uniform(-30, 30)  # randomize target direction
-
-        self.step_counter = 0
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
-        self.walk_cycle_step = 0
-
-        # 随机 beam 目标位置和朝向，增加训练多样性
-        beam_x = (random() - 0.5) * 10
-        beam_y = (random() - 0.5) * 10
-        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
-
-        for _ in range(5):
-            self._safe_receive_world_update(retries=2)
-            self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
-            self.Player.server.send()
-
-        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
-        finished_count = 0
-        for _ in range(50):
-            finished = self.Player.skills_manager.execute("Neutral")
-            self.sync()
-            if finished:
-                finished_count += 1
-                if finished_count >= 20:  # 假设需要连续20次完成才算成功
-                    break
-
-        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-            # Perturb waist + lower body only (10:), keep head/arms stable.
-            perturb_action[10:] = np.random.uniform(
-                -self.reset_joint_noise_rad,
-                self.reset_joint_noise_rad,
-                size=(self.no_of_actions - 10,)
-            )
-
-            for _ in range(self.reset_perturb_steps):
-                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-            for i in range(self.reset_recover_steps):
-                # Linearly fade perturbation to help policy start from near-neutral.
-                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-        # memory variables
-        self.sync()  
-        self.initial_position = np.array(self.Player.world.global_position[:2])
-        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions, np.float32)
-        # Build target in the robot's current forward direction instead of fixed global +x.
-        heading_deg = float(r.global_orientation_euler[2])
-        forward_offset = MathOps.rotate_2d_vec(np.array([length1, 0.0]), heading_deg, is_rad=False)
-        point1 = self.initial_position + forward_offset
-        point2 = point1 + MathOps.rotate_2d_vec(np.array([length2, 0]), angle2, is_rad=False)
-        point3 = point2 + MathOps.rotate_2d_vec(np.array([length3, 0]), angle3, is_rad=False)
-        self.point_list = [point1]
-        self.target_position = self.point_list[self.waypoint_index]
-        self.initial_height = self.Player.world.global_position[2]
-
-        return self.observe(True), {}
-
-    def render(self, mode='human', close=False):
-        return
-
-    def compute_reward(self, previous_pos, current_pos, action):
-        height = float(self.Player.world.global_position[2])
-
-        orientation_quat_inv = R.from_quat(self.Player.robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-        tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        ang_vel = np.deg2rad(self.Player.robot.gyroscope)
-        ang_vel_mag = float(np.linalg.norm(ang_vel))
-
-        is_fallen = height < 0.3
-        if is_fallen:
-            # remain = max(0, 800 - self.step_counter)
-            # return -8.0 - 0.01 * remain
-            return -1.0
-        
-
-
-        # # 目标方向
-        # to_target = self.target_position - current_pos
-        # dist_to_target = float(np.linalg.norm(to_target))
-        # if dist_to_target < 0.5:
-        #     return 15.0
-
-        # forward_dir = to_target / dist_to_target if dist_to_target > 0.1 else np.array([1.0, 0.0])
-        # delta_pos = current_pos - previous_pos
-        # forward_step = float(np.dot(delta_pos, forward_dir))
-        # lateral_step = float(np.linalg.norm(delta_pos - forward_dir * forward_step))
-
-        # 奖励项
-        # progress_reward = 2 * forward_step
-        # lateral_penalty = -0.1 * lateral_step
-        alive_bonus = 2.0
-
-        # action_penalty = -0.01 * float(np.linalg.norm(action))
-        smoothness_penalty = -0.01 * float(np.linalg.norm(action - self.last_action_for_reward))
-
-        posture_penalty = -0.3 * (tilt_mag)
-        ang_vel_penalty = -0.02 * ang_vel_mag
-
-        target_height = self.initial_height
-        height_error =  height - target_height
-        height_penalty = -0.5 * abs(height_error)  # 惩罚高度偏离，系数可调
-
-        # # 在 compute_reward 开头附近，添加高度变化率计算
-        # if not hasattr(self, 'last_height'):
-        #     self.last_height = height
-        #     self.last_height_time = self.step_counter  # 可选，用于时间间隔
-        # height_rate = height - self.last_height  # 正为上升，负为下降
-        # self.last_height = height
-
-        # 惩罚高度下降（负变化率）
-        # height_down_penalty = -5.0 * max(0, -height_rate)  # 系数可调，-height_rate 为正表示下降幅度
-
-        # # 在 compute_reward 中
-        # if self.step_counter > 50:
-        #     avg_prev_action = np.mean(self.prev_action_history, axis=0)
-        #     novelty = float(np.linalg.norm(action - avg_prev_action))
-        #     exploration_bonus = 0.05 * novelty
-        # else:
-        #     exploration_bonus = 0
-
-        # self.prev_action_history[self.history_idx] = action
-        # self.history_idx = (self.history_idx + 1) % 50
-
-
-        total = (
-            # progress_reward  + 
-                 alive_bonus + 
-                 # lateral_penalty +
-                 # action_penalty +
-                smoothness_penalty +
-                 posture_penalty
-                 + ang_vel_penalty
-                 + height_penalty
-                #  + exploration_bonus
-                # + height_down_penalty
-                 )
-        if time.time() - self.start_time >= 1200:
-            self.start_time = time.time()
-            print(
-                #   f"progress_reward:{progress_reward:.4f}",
-                #   f"lateral_penalty:{lateral_penalty:.4f}",
-                #   f"action_penalty:{action_penalty:.4f}"s, 
-                  f"height_penalty:{height_penalty:.4f}",
-                  f"smoothness_penalty:{smoothness_penalty:.4f},",
-                  f"posture_penalty:{posture_penalty:.4f}",
-                #   f"ang_vel_penalty:{ang_vel_penalty:.4f}",
-                #   f"height_down_penalty:{height_down_penalty:.4f}",
-                #   f"exploration_bonus:{exploration_bonus:.4f}"
-            )
-
-        return total
-
-
-
-    def step(self, action):
-
-        r = self.Player.robot
-        self.previous_action = action
-
-        self.target_joint_positions = (
-                # self.joint_nominal_position +
-                 self.scaling_factor * action
-        )
-        self.target_joint_positions *= self.train_sim_flip
-
-        for idx, target in enumerate(self.target_joint_positions):
-            r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=40, kd=1.0
-            )
-
-        self.previous_action = action
-
-        self.sync()  # run simulation step
-        self.step_counter += 1
-
-        if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
-            self.debug_joint_status()
-
-        current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
-
-        # Compute reward based on movement from previous step
-        reward = self.compute_reward(self.previous_pos, current_pos, action)
-
-        # Update previous position
-        self.previous_pos = current_pos.copy()
-        self.last_action_for_reward = action.copy()
-
-        # Fall detection and penalty
-        is_fallen = self.Player.world.global_position[2] < 0.3
-
-        # terminal state: the robot is falling or timeout
-        terminated = is_fallen or self.step_counter > 800 or self.route_completed
-        truncated = False
-
-        return self.observe(), reward, terminated, truncated, {}
-
-
-class Train(Train_Base):
-    def __init__(self, script) -> None:
-        super().__init__(script)
-
-    def train(self, args):
-
-        # --------------------------------------- Learning parameters
-        n_envs = int(os.environ.get("GYM_CPU_N_ENVS", "20"))
-        if n_envs < 1:
-            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
-        server_warmup_sec = float(os.environ.get("GYM_CPU_SERVER_WARMUP_SEC", "3.0"))
-        n_steps_per_env = 256  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = 512  # should be a factor of (n_steps_per_env * n_envs)
-        total_steps = 30000000
-        learning_rate = 1e-4
-        folder_name = f'Walk_R{self.robot_type}'
-        model_path = f'./scripts/gyms/logs/{folder_name}/'
-
-        print(f"Model path: {model_path}")
-        print(f"Using {n_envs} parallel environments")
-
-        # --------------------------------------- Run algorithm
-        def init_env(i_env, monitor=False):
-            def thunk():
-                env = WalkEnv(self.ip, self.server_p + i_env)
-                if monitor:
-                    env = Monitor(env)
-                return env
-
-            return thunk
-
-        server_log_dir = os.path.join(model_path, "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1)  # include 1 extra server for testing
-
-        # Wait for servers to start
-        print(f"Starting {n_envs + 1} rcssservermj servers...")
-        if server_warmup_sec > 0:
-            print(f"Waiting {server_warmup_sec:.1f}s for server warmup...")
-            sleep(server_warmup_sec)
-        print("Servers started, creating environments...")
-
-        env = SubprocVecEnv([init_env(i, monitor=True) for i in range(n_envs)])
-        # Use single-process eval env to avoid extra subprocess fragility during callback evaluation.
-        eval_env = DummyVecEnv([init_env(n_envs, monitor=True)])
-
-        try:
-            # Custom policy network architecture
-            policy_kwargs = dict(
-                net_arch=dict(
-                    pi=[512, 256, 128],  # Policy network: 3 layers
-                    vf=[512, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
-            )
-
-            if "model_file" in args:  # retrain
-                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
-                                 batch_size=minibatch_size, learning_rate=learning_rate)
-            else:  # train new model
-                model = PPO(
-                    "MlpPolicy",
-                    env=env,
-                    verbose=1,
-                    n_steps=n_steps_per_env,
-                    batch_size=minibatch_size,
-                    learning_rate=learning_rate,
-                    device="cpu",
-                    policy_kwargs=policy_kwargs,
-                    ent_coef=0.03,  # Entropy coefficient for exploration
-                    clip_range=0.13,  # PPO clipping parameter
-                    gae_lambda=0.95,  # GAE lambda
-                    gamma=0.95 , # Discount factor
-                    target_kl=0.03,
-                    n_epochs=5,
-                    tensorboard_log=f"./scripts/gyms/logs/{folder_name}/tensorboard/"
-                )
-
-            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
-                                          eval_freq=n_steps_per_env * 10, save_freq=n_steps_per_env * 10,
-                                          backup_env_file=__file__)
-        except KeyboardInterrupt:
-            sleep(1)  # wait for child processes
-            print("\nctrl+c pressed, aborting...\n")
-            servers.kill()
-            return
-
-        env.close()
-        eval_env.close()
-        servers.kill()
-
-    def test(self, args):
-
-        # Uses different server and monitor ports
-        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        server = Train_Server(self.server_p - 1, self.monitor_p, 1)
-        env = WalkEnv(self.ip, self.server_p - 1)
-        model = PPO.load(args["model_file"], env=env)
-
-        try:
-            self.export_model(args["model_file"], args["model_file"] + ".pkl",
-                              False)  # Export to pkl to create custom behavior
-            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
-        except KeyboardInterrupt:
-            print()
-
-        env.close()
-        server.kill()
-
-
-if __name__ == "__main__":
-    from types import SimpleNamespace
-
-    # 创建默认参数
-    script_args = SimpleNamespace(
-        args=SimpleNamespace(
-            i='127.0.0.1',  # Server IP
-            p=3100,  # Server port
-            m=3200,  # Monitor port
-            r=0,  # Robot type
-            t='Gym',  # Team name
-            u=1  # Uniform number
-        )
-    )
-
-    trainer = Train(script_args)
-    trainer.train({"model_file": "scripts/gyms/logs/Walk_R0_004/best_model.zip"})
-    # trainer.test({"model_file": "scripts/gyms/logs/Walk_R0_004/best_model.zip",
-                #   "folder_dir": "scripts/gyms/logs/Walk_R0_004/",})

scripts/gyms/logs/Walk_R0_006/Walk.py

@@ -1,679 +0,0 @@
-import os
-import numpy as np
-import math
-import time
-from time import sleep
-from random import random
-from random import uniform
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.monitor import Monitor
-from stable_baselines3.common.vec_env import SubprocVecEnv, DummyVecEnv
-
-import gymnasium as gym
-from gymnasium import spaces
-
-from scripts.commons.Train_Base import Train_Base
-from scripts.commons.Server import Server as Train_Server
-
-from agent.base_agent import Base_Agent
-from utils.math_ops import MathOps
-
-from scipy.spatial.transform import Rotation as R
-
-'''
-Objective:
-Learn how to run forward using step primitive
-----------
-- class Basic_Run: implements an OpenAI custom gym
-- class Train:  implements algorithms to train a new model or test an existing model
-'''
-
-
-class WalkEnv(gym.Env):
-    def __init__(self, ip, server_p) -> None:
-
-        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
-        self.Player = player = Base_Agent(
-            team_name="Gym",
-            number=1,
-            host=ip,
-            port=server_p
-        )
-        self.robot_type = self.Player.robot
-        self.step_counter = 0  # to limit episode size
-        self.force_play_on = True
-
-        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
-        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
-        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
-        self.isfallen = False
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.debug_every_n_steps = 5
-        self.enable_debug_joint_status = False
-        self.calibrate_nominal_from_neutral = True
-        self.auto_calibrate_train_sim_flip = True
-        self.nominal_calibrated_once = False
-        self.flip_calibrated_once = False
-        self._target_hz = 0.0
-        self._target_dt = 0.0
-        self._last_sync_time = None
-        target_hz_env = 0
-        if target_hz_env:
-            try:
-                self._target_hz = float(target_hz_env)
-            except ValueError:
-                self._target_hz = 0.0
-        if self._target_hz > 0.0:
-            self._target_dt = 1.0 / self._target_hz
-
-        # State space
-        # 原始观测大小: 78
-        obs_size = 78
-        self.obs = np.zeros(obs_size, np.float32)
-        self.observation_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(obs_size,),
-            dtype=np.float32
-        )
-
-        action_dim = len(self.Player.robot.ROBOT_MOTORS)
-        self.no_of_actions = action_dim
-        self.action_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(action_dim,),
-            dtype=np.float32
-        )
-
-        # 中立姿态
-        self.joint_nominal_position = np.array(
-            [
-                0.0,
-                0.0,
-                0.0,
-                1.4,
-                0.0,
-                -0.4,
-                0.0,
-                -1.4,
-                0.0,
-                0.4,
-                0.0,
-                -0.4,
-                0.0,
-                0.0,
-                0.8,
-                -0.4,
-                0.0,
-                0.4,
-                0.0,
-                0.0,
-                -0.8,
-                0.4,
-                0.0,
-            ]
-        )
-        self.joint_nominal_position = np.zeros(self.no_of_actions)
-        self.train_sim_flip = np.array(
-            [
-                1.0,  # 0: Head_yaw (he1)
-                -1.0,  # 1: Head_pitch (he2)
-                1.0,  # 2: Left_Shoulder_Pitch (lae1)
-                -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                -1.0,  # 4: Left_Elbow_Pitch (lae3)
-                1.0,  # 5: Left_Elbow_Yaw (lae4)
-                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                -1.0,  # 7: Right_Shoulder_Roll (rae2)
-                1.0,  # 8: Right_Elbow_Pitch (rae3)
-                1.0,  # 9: Right_Elbow_Yaw (rae4)
-                1.0,  # 10: Waist (te1)
-                1.0,  # 11: Left_Hip_Pitch (lle1)
-                -1.0,  # 12: Left_Hip_Roll (lle2)
-                -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,  # 14: Left_Knee_Pitch (lle4)
-                1.0,  # 15: Left_Ankle_Pitch (lle5)
-                -1.0,  # 16: Left_Ankle_Roll (lle6)
-                -1.0,  # 17: Right_Hip_Pitch (rle1)
-                -1.0,  # 18: Right_Hip_Roll (rle2)
-                -1.0,  # 19: Right_Hip_Yaw (rle3)
-                -1.0,  # 20: Right_Knee_Pitch (rle4)
-                -1.0,  # 21: Right_Ankle_Pitch (rle5)
-                -1.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-
-        self.scaling_factor = 0.3
-        # self.scaling_factor = 1
-
-        # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = True
-        self.reset_beam_yaw_range_deg = 180  # randomize target direction fully to encourage learning a real walk instead of a fixed gait
-        self.reset_joint_noise_rad = 0.015
-        self.reset_perturb_steps = 3
-        self.reset_recover_steps = 8
-
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Track previous position
-        self.Player.server.connect()
-        # sleep(2.0)  # Longer wait for connection to establish completely
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-        self.start_time = time.time()
-
-    def _reconnect_server(self):
-        try:
-            self.Player.server.shutdown()
-        except Exception:
-            pass
-
-        self.Player.server.connect()
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-
-    def _safe_receive_world_update(self, retries=1):
-        last_exc = None
-        for attempt in range(retries + 1):
-            try:
-                self.Player.server.receive()
-                self.Player.world.update()
-                return
-            except (ConnectionResetError, OSError) as exc:
-                last_exc = exc
-                if attempt >= retries:
-                    raise
-                self._reconnect_server()
-        if last_exc is not None:
-            raise last_exc
-
-    def debug_log(self, message):
-        print(message)
-        try:
-            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
-            with open(log_path, "a", encoding="utf-8") as f:
-                f.write(message + "\n")
-        except OSError:
-            pass
-
-    def observe(self, init=False):
-
-        """获取当前观测值"""
-        robot = self.Player.robot
-        world = self.Player.world
-
-        # Safety check: ensure data is available
-
-        # 计算目标速度
-        raw_target = self.target_position - world.global_position[:2]
-        velocity = MathOps.rotate_2d_vec(
-            raw_target,
-            -robot.global_orientation_euler[2],
-            is_rad=False
-        )
-
-        # 计算相对方向
-        rel_orientation = MathOps.vector_angle(velocity) * 0.3
-        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
-
-        velocity = np.concatenate([velocity, np.array([rel_orientation])])
-        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
-        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
-
-        # 关节状态
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        qpos_qvel_previous_action = np.concatenate([
-            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
-            radian_joint_speeds / 110.0 * self.train_sim_flip,
-            self.previous_action / 10.0,
-        ])
-
-        # 角速度
-        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
-
-        # 投影的重力方向
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-
-        # 组合观测
-        observation = np.concatenate([
-            qpos_qvel_previous_action,
-            ang_vel,
-            velocity,
-            projected_gravity,
-        ])
-
-        observation = np.clip(observation, -10.0, 10.0)
-        return observation.astype(np.float32)
-
-    def sync(self):
-        ''' Run a single simulation step '''
-        self._safe_receive_world_update(retries=1)
-        self.Player.robot.commit_motor_targets_pd()
-        self.Player.server.send()
-        if self._target_dt > 0.0:
-            now = time.time()
-            if self._last_sync_time is None:
-                self._last_sync_time = now
-                return
-            elapsed = now - self._last_sync_time
-            remaining = self._target_dt - elapsed
-            if remaining > 0.0:
-                time.sleep(remaining)
-                now = time.time()
-            self._last_sync_time = now
-
-    def debug_joint_status(self):
-        robot = self.Player.robot
-        actual_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        target_joint_positions = getattr(
-            self,
-            'target_joint_positions',
-            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
-        )
-        joint_error = actual_joint_positions - target_joint_positions
-        leg_slice = slice(11, None)
-
-        self.debug_log(
-            "[WalkDebug] "
-            f"step={self.step_counter} "
-            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
-            f"target_xy={np.round(self.target_position, 3).tolist()} "
-            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
-            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
-            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
-            f"fallen={self.Player.world.global_position[2] < 0.3}"
-        )
-        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
-
-    def reset(self, seed=None, options=None):
-        '''
-        Reset and stabilize the robot
-        Note: for some behaviors it would be better to reduce stabilization or add noise
-        '''
-        r = self.Player.robot
-        super().reset(seed=seed)
-        if seed is not None:
-            np.random.seed(seed)
-
-        length1 = 2  # randomize target distance
-        length2 = np.random.uniform(0.6, 1)  # randomize target distance
-        length3 = np.random.uniform(0.6, 1)  # randomize target distance
-        angle2 = np.random.uniform(-30, 30)  # randomize initial orientation
-        angle3 = np.random.uniform(-30, 30)  # randomize target direction
-
-        self.step_counter = 0
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
-        self.walk_cycle_step = 0
-
-        # 随机 beam 目标位置和朝向，增加训练多样性
-        beam_x = (random() - 0.5) * 10
-        beam_y = (random() - 0.5) * 10
-        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
-
-        for _ in range(5):
-            self._safe_receive_world_update(retries=2)
-            self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
-            self.Player.server.send()
-
-        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
-        finished_count = 0
-        for _ in range(50):
-            finished = self.Player.skills_manager.execute("Neutral")
-            self.sync()
-            if finished:
-                finished_count += 1
-                if finished_count >= 20:  # 假设需要连续20次完成才算成功
-                    break
-
-        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-            # Perturb waist + lower body only (10:), keep head/arms stable.
-            perturb_action[10:] = np.random.uniform(
-                -self.reset_joint_noise_rad,
-                self.reset_joint_noise_rad,
-                size=(self.no_of_actions - 10,)
-            )
-
-            for _ in range(self.reset_perturb_steps):
-                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-            for i in range(self.reset_recover_steps):
-                # Linearly fade perturbation to help policy start from near-neutral.
-                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-        # memory variables
-        self.sync()  
-        self.initial_position = np.array(self.Player.world.global_position[:2])
-        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions, np.float32)
-        # Build target in the robot's current forward direction instead of fixed global +x.
-        heading_deg = float(r.global_orientation_euler[2])
-        forward_offset = MathOps.rotate_2d_vec(np.array([length1, 0.0]), heading_deg, is_rad=False)
-        point1 = self.initial_position + forward_offset
-        point2 = point1 + MathOps.rotate_2d_vec(np.array([length2, 0]), angle2, is_rad=False)
-        point3 = point2 + MathOps.rotate_2d_vec(np.array([length3, 0]), angle3, is_rad=False)
-        self.point_list = [point1]
-        self.target_position = self.point_list[self.waypoint_index]
-        self.initial_height = self.Player.world.global_position[2]
-
-        return self.observe(True), {}
-
-    def render(self, mode='human', close=False):
-        return
-
-    def compute_reward(self, previous_pos, current_pos, action):
-        height = float(self.Player.world.global_position[2])
-
-        orientation_quat_inv = R.from_quat(self.Player.robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-        tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        ang_vel = np.deg2rad(self.Player.robot.gyroscope)
-        ang_vel_mag = float(np.linalg.norm(ang_vel))
-
-        is_fallen = height < 0.3
-        if is_fallen:
-            # remain = max(0, 800 - self.step_counter)
-            # return -8.0 - 0.01 * remain
-            return -1.0
-        
-
-
-        # # 目标方向
-        # to_target = self.target_position - current_pos
-        # dist_to_target = float(np.linalg.norm(to_target))
-        # if dist_to_target < 0.5:
-        #     return 15.0
-
-        # forward_dir = to_target / dist_to_target if dist_to_target > 0.1 else np.array([1.0, 0.0])
-        # delta_pos = current_pos - previous_pos
-        # forward_step = float(np.dot(delta_pos, forward_dir))
-        # lateral_step = float(np.linalg.norm(delta_pos - forward_dir * forward_step))
-
-        # 奖励项
-        # progress_reward = 2 * forward_step
-        # lateral_penalty = -0.1 * lateral_step
-        alive_bonus = 2.0
-
-        # action_penalty = -0.01 * float(np.linalg.norm(action))
-        smoothness_penalty = -0.01 * float(np.linalg.norm(action - self.last_action_for_reward))
-
-        posture_penalty = -0.3 * (tilt_mag)
-        ang_vel_penalty = -0.02 * ang_vel_mag
-
-        target_height = self.initial_height
-        height_error =  height - target_height
-        height_penalty = -0.5 * abs(height_error)  # 惩罚高度偏离，系数可调
-
-        # # 在 compute_reward 开头附近，添加高度变化率计算
-        # if not hasattr(self, 'last_height'):
-        #     self.last_height = height
-        #     self.last_height_time = self.step_counter  # 可选，用于时间间隔
-        # height_rate = height - self.last_height  # 正为上升，负为下降
-        # self.last_height = height
-
-        # 惩罚高度下降（负变化率）
-        # height_down_penalty = -5.0 * max(0, -height_rate)  # 系数可调，-height_rate 为正表示下降幅度
-
-        # # 在 compute_reward 中
-        # if self.step_counter > 50:
-        #     avg_prev_action = np.mean(self.prev_action_history, axis=0)
-        #     novelty = float(np.linalg.norm(action - avg_prev_action))
-        #     exploration_bonus = 0.05 * novelty
-        # else:
-        #     exploration_bonus = 0
-
-        # self.prev_action_history[self.history_idx] = action
-        # self.history_idx = (self.history_idx + 1) % 50
-
-
-        total = (
-            # progress_reward  + 
-                 alive_bonus + 
-                 # lateral_penalty +
-                 # action_penalty +
-                smoothness_penalty +
-                 posture_penalty
-                 + ang_vel_penalty
-                 + height_penalty
-                #  + exploration_bonus
-                # + height_down_penalty
-                 )
-        if time.time() - self.start_time >= 600:
-            self.start_time = time.time()
-            print(
-                #   f"progress_reward:{progress_reward:.4f}",
-                #   f"lateral_penalty:{lateral_penalty:.4f}",
-                #   f"action_penalty:{action_penalty:.4f}"s, 
-                  f"height_penalty:{height_penalty:.4f}",
-                  f"smoothness_penalty:{smoothness_penalty:.4f},",
-                  f"posture_penalty:{posture_penalty:.4f}",
-                #   f"ang_vel_penalty:{ang_vel_penalty:.4f}",
-                #   f"height_down_penalty:{height_down_penalty:.4f}",
-                #   f"exploration_bonus:{exploration_bonus:.4f}"
-            )
-
-        return total
-
-
-
-    def step(self, action):
-
-        r = self.Player.robot
-        self.previous_action = action
-
-        self.target_joint_positions = (
-                # self.joint_nominal_position +
-                 self.scaling_factor * action
-        )
-        self.target_joint_positions *= self.train_sim_flip
-
-        for idx, target in enumerate(self.target_joint_positions):
-            r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=40, kd=1.0
-            )
-
-        self.previous_action = action
-
-        self.sync()  # run simulation step
-        self.step_counter += 1
-
-        if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
-            self.debug_joint_status()
-
-        current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
-
-        # Compute reward based on movement from previous step
-        reward = self.compute_reward(self.previous_pos, current_pos, action)
-
-        # Update previous position
-        self.previous_pos = current_pos.copy()
-        self.last_action_for_reward = action.copy()
-
-        # Fall detection and penalty
-        is_fallen = self.Player.world.global_position[2] < 0.3
-
-        # terminal state: the robot is falling or timeout
-        terminated = is_fallen or self.step_counter > 800 or self.route_completed
-        truncated = False
-
-        return self.observe(), reward, terminated, truncated, {}
-
-
-class Train(Train_Base):
-    def __init__(self, script) -> None:
-        super().__init__(script)
-
-    def train(self, args):
-
-        # --------------------------------------- Learning parameters
-        n_envs = int(os.environ.get("GYM_CPU_N_ENVS", "20"))
-        if n_envs < 1:
-            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
-        server_warmup_sec = float(os.environ.get("GYM_CPU_SERVER_WARMUP_SEC", "3.0"))
-        n_steps_per_env = int(os.environ.get("GYM_CPU_TRAIN_STEPS_PER_ENV", "256"))  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = int(os.environ.get("GYM_CPU_TRAIN_BATCH_SIZE", "512"))  # should be a factor of (n_steps_per_env * n_envs)
-        total_steps = 30000000
-        learning_rate = float(os.environ.get("GYM_CPU_TRAIN_LR", "3e-4"))
-        folder_name = f'Walk_R{self.robot_type}'
-        model_path = f'./scripts/gyms/logs/{folder_name}/'
-
-        print(f"Model path: {model_path}")
-        print(f"Using {n_envs} parallel environments")
-
-        # --------------------------------------- Run algorithm
-        def init_env(i_env, monitor=False):
-            def thunk():
-                env = WalkEnv(self.ip, self.server_p + i_env)
-                if monitor:
-                    env = Monitor(env)
-                return env
-
-            return thunk
-
-        server_log_dir = os.path.join(model_path, "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1, no_render=True, no_realtime=True)  # include 1 extra server for testing
-
-        # Wait for servers to start
-        print(f"Starting {n_envs + 1} rcssservermj servers...")
-        if server_warmup_sec > 0:
-            print(f"Waiting {server_warmup_sec:.1f}s for server warmup...")
-            sleep(server_warmup_sec)
-        print("Servers started, creating environments...")
-
-        env = SubprocVecEnv([init_env(i, monitor=True) for i in range(n_envs)])
-        # Use single-process eval env to avoid extra subprocess fragility during callback evaluation.
-        eval_env = DummyVecEnv([init_env(n_envs, monitor=True)])
-
-        try:
-            # Custom policy network architecture
-            policy_kwargs = dict(
-                net_arch=dict(
-                    pi=[512, 256, 128],  # Policy network: 3 layers
-                    vf=[512, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
-            )
-
-            if "model_file" in args:  # retrain
-                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
-                                 batch_size=minibatch_size, learning_rate=learning_rate)
-            else:  # train new model
-                model = PPO(
-                    "MlpPolicy",
-                    env=env,
-                    verbose=1,
-                    n_steps=n_steps_per_env,
-                    batch_size=minibatch_size,
-                    learning_rate=learning_rate,
-                    device="cpu",
-                    policy_kwargs=policy_kwargs,
-                    ent_coef=float(os.environ.get("GYM_CPU_TRAIN_ENT_COEF", "0.05")),  # Entropy coefficient for exploration
-                    clip_range=float(os.environ.get("GYM_CPU_TRAIN_CLIP_RANGE", "0.2")),  # PPO clipping parameter
-                    gae_lambda=0.95,  # GAE lambda
-                    gamma=float(os.environ.get("GYM_CPU_TRAIN_GAMMA", "0.95")), # Discount factor
-                    target_kl=0.03,
-                    n_epochs=int(os.environ.get("GYM_CPU_TRAIN_EPOCHS", "5")),
-                    # tensorboard_log=f"./scripts/gyms/logs/{folder_name}/tensorboard/"
-                )
-
-            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
-                                          eval_freq=n_steps_per_env * 20, save_freq=n_steps_per_env * 20,
-                                          backup_env_file=__file__)
-        except KeyboardInterrupt:
-            sleep(1)  # wait for child processes
-            print("\nctrl+c pressed, aborting...\n")
-            servers.kill()
-            return
-
-        env.close()
-        eval_env.close()
-        servers.kill()
-
-    def test(self, args):
-
-        # Uses different server and monitor ports
-        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        test_no_render = os.environ.get("GYM_CPU_TEST_NO_RENDER", "0") == "1"
-        test_no_realtime = os.environ.get("GYM_CPU_TEST_NO_REALTIME", "0") == "1"
-
-        server = Train_Server(
-            self.server_p - 1,
-            self.monitor_p,
-            1,
-            no_render=test_no_render,
-            no_realtime=test_no_realtime,
-        )
-        env = WalkEnv(self.ip, self.server_p - 1)
-        model = PPO.load(args["model_file"], env=env)
-
-        try:
-            self.export_model(args["model_file"], args["model_file"] + ".pkl",
-                              False)  # Export to pkl to create custom behavior
-            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
-        except KeyboardInterrupt:
-            print()
-
-        env.close()
-        server.kill()
-
-
-if __name__ == "__main__":
-    from types import SimpleNamespace
-
-    # 创建默认参数
-    script_args = SimpleNamespace(
-        args=SimpleNamespace(
-            i='127.0.0.1',  # Server IP
-            p=3100,  # Server port
-            m=3200,  # Monitor port
-            r=0,  # Robot type
-            t='Gym',  # Team name
-            u=1  # Uniform number
-        )
-    )
-
-    trainer = Train(script_args)
-
-    run_mode = os.environ.get("GYM_CPU_MODE", "train").strip().lower()
-
-    if run_mode == "test":
-        test_model_file = os.environ.get("GYM_CPU_TEST_MODEL", "scripts/gyms/logs/Walk_R0_004/best_model.zip")
-        test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Walk_R0_004/")
-        trainer.test({"model_file": test_model_file, "folder_dir": test_folder})
-    else:
-        retrain_model = os.environ.get("GYM_CPU_TRAIN_MODEL", "").strip()
-        if retrain_model:
-            trainer.train({"model_file": retrain_model})
-        else:
-            trainer.train({})

scripts/gyms/logs/Walk_R0_007/Walk.py

@@ -1,679 +0,0 @@
-import os
-import numpy as np
-import math
-import time
-from time import sleep
-from random import random
-from random import uniform
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.monitor import Monitor
-from stable_baselines3.common.vec_env import SubprocVecEnv, DummyVecEnv
-
-import gymnasium as gym
-from gymnasium import spaces
-
-from scripts.commons.Train_Base import Train_Base
-from scripts.commons.Server import Server as Train_Server
-
-from agent.base_agent import Base_Agent
-from utils.math_ops import MathOps
-
-from scipy.spatial.transform import Rotation as R
-
-'''
-Objective:
-Learn how to run forward using step primitive
-----------
-- class Basic_Run: implements an OpenAI custom gym
-- class Train:  implements algorithms to train a new model or test an existing model
-'''
-
-
-class WalkEnv(gym.Env):
-    def __init__(self, ip, server_p) -> None:
-
-        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
-        self.Player = player = Base_Agent(
-            team_name="Gym",
-            number=1,
-            host=ip,
-            port=server_p
-        )
-        self.robot_type = self.Player.robot
-        self.step_counter = 0  # to limit episode size
-        self.force_play_on = True
-
-        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
-        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
-        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
-        self.isfallen = False
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.debug_every_n_steps = 5
-        self.enable_debug_joint_status = False
-        self.calibrate_nominal_from_neutral = True
-        self.auto_calibrate_train_sim_flip = True
-        self.nominal_calibrated_once = False
-        self.flip_calibrated_once = False
-        self._target_hz = 0.0
-        self._target_dt = 0.0
-        self._last_sync_time = None
-        target_hz_env = 0
-        if target_hz_env:
-            try:
-                self._target_hz = float(target_hz_env)
-            except ValueError:
-                self._target_hz = 0.0
-        if self._target_hz > 0.0:
-            self._target_dt = 1.0 / self._target_hz
-
-        # State space
-        # 原始观测大小: 78
-        obs_size = 78
-        self.obs = np.zeros(obs_size, np.float32)
-        self.observation_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(obs_size,),
-            dtype=np.float32
-        )
-
-        action_dim = len(self.Player.robot.ROBOT_MOTORS)
-        self.no_of_actions = action_dim
-        self.action_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(action_dim,),
-            dtype=np.float32
-        )
-
-        # 中立姿态
-        self.joint_nominal_position = np.array(
-            [
-                0.0,
-                0.0,
-                0.0,
-                1.4,
-                0.0,
-                -0.4,
-                0.0,
-                -1.4,
-                0.0,
-                0.4,
-                0.0,
-                -0.4,
-                0.0,
-                0.0,
-                0.8,
-                -0.4,
-                0.0,
-                0.4,
-                0.0,
-                0.0,
-                -0.8,
-                0.4,
-                0.0,
-            ]
-        )
-        self.joint_nominal_position = np.zeros(self.no_of_actions)
-        self.train_sim_flip = np.array(
-            [
-                1.0,  # 0: Head_yaw (he1)
-                -1.0,  # 1: Head_pitch (he2)
-                1.0,  # 2: Left_Shoulder_Pitch (lae1)
-                -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                -1.0,  # 4: Left_Elbow_Pitch (lae3)
-                1.0,  # 5: Left_Elbow_Yaw (lae4)
-                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                -1.0,  # 7: Right_Shoulder_Roll (rae2)
-                1.0,  # 8: Right_Elbow_Pitch (rae3)
-                1.0,  # 9: Right_Elbow_Yaw (rae4)
-                1.0,  # 10: Waist (te1)
-                1.0,  # 11: Left_Hip_Pitch (lle1)
-                -1.0,  # 12: Left_Hip_Roll (lle2)
-                -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,  # 14: Left_Knee_Pitch (lle4)
-                1.0,  # 15: Left_Ankle_Pitch (lle5)
-                -1.0,  # 16: Left_Ankle_Roll (lle6)
-                -1.0,  # 17: Right_Hip_Pitch (rle1)
-                -1.0,  # 18: Right_Hip_Roll (rle2)
-                -1.0,  # 19: Right_Hip_Yaw (rle3)
-                -1.0,  # 20: Right_Knee_Pitch (rle4)
-                -1.0,  # 21: Right_Ankle_Pitch (rle5)
-                -1.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-
-        self.scaling_factor = 0.3
-        # self.scaling_factor = 1
-
-        # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = True
-        self.reset_beam_yaw_range_deg = 180  # randomize target direction fully to encourage learning a real walk instead of a fixed gait
-        self.reset_joint_noise_rad = 0.015
-        self.reset_perturb_steps = 3
-        self.reset_recover_steps = 8
-
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Track previous position
-        self.Player.server.connect()
-        # sleep(2.0)  # Longer wait for connection to establish completely
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-        self.start_time = time.time()
-
-    def _reconnect_server(self):
-        try:
-            self.Player.server.shutdown()
-        except Exception:
-            pass
-
-        self.Player.server.connect()
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-
-    def _safe_receive_world_update(self, retries=1):
-        last_exc = None
-        for attempt in range(retries + 1):
-            try:
-                self.Player.server.receive()
-                self.Player.world.update()
-                return
-            except (ConnectionResetError, OSError) as exc:
-                last_exc = exc
-                if attempt >= retries:
-                    raise
-                self._reconnect_server()
-        if last_exc is not None:
-            raise last_exc
-
-    def debug_log(self, message):
-        print(message)
-        try:
-            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
-            with open(log_path, "a", encoding="utf-8") as f:
-                f.write(message + "\n")
-        except OSError:
-            pass
-
-    def observe(self, init=False):
-
-        """获取当前观测值"""
-        robot = self.Player.robot
-        world = self.Player.world
-
-        # Safety check: ensure data is available
-
-        # 计算目标速度
-        raw_target = self.target_position - world.global_position[:2]
-        velocity = MathOps.rotate_2d_vec(
-            raw_target,
-            -robot.global_orientation_euler[2],
-            is_rad=False
-        )
-
-        # 计算相对方向
-        rel_orientation = MathOps.vector_angle(velocity) * 0.3
-        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
-
-        velocity = np.concatenate([velocity, np.array([rel_orientation])])
-        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
-        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
-
-        # 关节状态
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        qpos_qvel_previous_action = np.concatenate([
-            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
-            radian_joint_speeds / 110.0 * self.train_sim_flip,
-            self.previous_action / 10.0,
-        ])
-
-        # 角速度
-        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
-
-        # 投影的重力方向
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-
-        # 组合观测
-        observation = np.concatenate([
-            qpos_qvel_previous_action,
-            ang_vel,
-            velocity,
-            projected_gravity,
-        ])
-
-        observation = np.clip(observation, -10.0, 10.0)
-        return observation.astype(np.float32)
-
-    def sync(self):
-        ''' Run a single simulation step '''
-        self._safe_receive_world_update(retries=1)
-        self.Player.robot.commit_motor_targets_pd()
-        self.Player.server.send()
-        if self._target_dt > 0.0:
-            now = time.time()
-            if self._last_sync_time is None:
-                self._last_sync_time = now
-                return
-            elapsed = now - self._last_sync_time
-            remaining = self._target_dt - elapsed
-            if remaining > 0.0:
-                time.sleep(remaining)
-                now = time.time()
-            self._last_sync_time = now
-
-    def debug_joint_status(self):
-        robot = self.Player.robot
-        actual_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        target_joint_positions = getattr(
-            self,
-            'target_joint_positions',
-            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
-        )
-        joint_error = actual_joint_positions - target_joint_positions
-        leg_slice = slice(11, None)
-
-        self.debug_log(
-            "[WalkDebug] "
-            f"step={self.step_counter} "
-            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
-            f"target_xy={np.round(self.target_position, 3).tolist()} "
-            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
-            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
-            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
-            f"fallen={self.Player.world.global_position[2] < 0.3}"
-        )
-        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
-
-    def reset(self, seed=None, options=None):
-        '''
-        Reset and stabilize the robot
-        Note: for some behaviors it would be better to reduce stabilization or add noise
-        '''
-        r = self.Player.robot
-        super().reset(seed=seed)
-        if seed is not None:
-            np.random.seed(seed)
-
-        length1 = 2  # randomize target distance
-        length2 = np.random.uniform(0.6, 1)  # randomize target distance
-        length3 = np.random.uniform(0.6, 1)  # randomize target distance
-        angle2 = np.random.uniform(-30, 30)  # randomize initial orientation
-        angle3 = np.random.uniform(-30, 30)  # randomize target direction
-
-        self.step_counter = 0
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
-        self.walk_cycle_step = 0
-
-        # 随机 beam 目标位置和朝向，增加训练多样性
-        beam_x = (random() - 0.5) * 10
-        beam_y = (random() - 0.5) * 10
-        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
-
-        for _ in range(5):
-            self._safe_receive_world_update(retries=2)
-            self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
-            self.Player.server.send()
-
-        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
-        finished_count = 0
-        for _ in range(50):
-            finished = self.Player.skills_manager.execute("Neutral")
-            self.sync()
-            if finished:
-                finished_count += 1
-                if finished_count >= 20:  # 假设需要连续20次完成才算成功
-                    break
-
-        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-            # Perturb waist + lower body only (10:), keep head/arms stable.
-            perturb_action[10:] = np.random.uniform(
-                -self.reset_joint_noise_rad,
-                self.reset_joint_noise_rad,
-                size=(self.no_of_actions - 10,)
-            )
-
-            for _ in range(self.reset_perturb_steps):
-                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-            for i in range(self.reset_recover_steps):
-                # Linearly fade perturbation to help policy start from near-neutral.
-                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-        # memory variables
-        self.sync()  
-        self.initial_position = np.array(self.Player.world.global_position[:2])
-        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions, np.float32)
-        # Build target in the robot's current forward direction instead of fixed global +x.
-        heading_deg = float(r.global_orientation_euler[2])
-        forward_offset = MathOps.rotate_2d_vec(np.array([length1, 0.0]), heading_deg, is_rad=False)
-        point1 = self.initial_position + forward_offset
-        point2 = point1 + MathOps.rotate_2d_vec(np.array([length2, 0]), angle2, is_rad=False)
-        point3 = point2 + MathOps.rotate_2d_vec(np.array([length3, 0]), angle3, is_rad=False)
-        self.point_list = [point1]
-        self.target_position = self.point_list[self.waypoint_index]
-        self.initial_height = self.Player.world.global_position[2]
-
-        return self.observe(True), {}
-
-    def render(self, mode='human', close=False):
-        return
-
-    def compute_reward(self, previous_pos, current_pos, action):
-        height = float(self.Player.world.global_position[2])
-
-        orientation_quat_inv = R.from_quat(self.Player.robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-        tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        ang_vel = np.deg2rad(self.Player.robot.gyroscope)
-        ang_vel_mag = float(np.linalg.norm(ang_vel))
-
-        is_fallen = height < 0.3
-        if is_fallen:
-            # remain = max(0, 800 - self.step_counter)
-            # return -8.0 - 0.01 * remain
-            return -1.0
-        
-
-
-        # # 目标方向
-        # to_target = self.target_position - current_pos
-        # dist_to_target = float(np.linalg.norm(to_target))
-        # if dist_to_target < 0.5:
-        #     return 15.0
-
-        # forward_dir = to_target / dist_to_target if dist_to_target > 0.1 else np.array([1.0, 0.0])
-        # delta_pos = current_pos - previous_pos
-        # forward_step = float(np.dot(delta_pos, forward_dir))
-        # lateral_step = float(np.linalg.norm(delta_pos - forward_dir * forward_step))
-
-        # 奖励项
-        # progress_reward = 2 * forward_step
-        # lateral_penalty = -0.1 * lateral_step
-        alive_bonus = 2.0
-
-        # action_penalty = -0.01 * float(np.linalg.norm(action))
-        smoothness_penalty = -0.01 * float(np.linalg.norm(action - self.last_action_for_reward))
-
-        posture_penalty = -0.3 * (tilt_mag)
-        ang_vel_penalty = -0.02 * ang_vel_mag
-
-        target_height = self.initial_height
-        height_error =  height - target_height
-        height_penalty = -1 * abs(height_error)  # 惩罚高度偏离，系数可调
-
-        # # 在 compute_reward 开头附近，添加高度变化率计算
-        # if not hasattr(self, 'last_height'):
-        #     self.last_height = height
-        #     self.last_height_time = self.step_counter  # 可选，用于时间间隔
-        # height_rate = height - self.last_height  # 正为上升，负为下降
-        # self.last_height = height
-
-        # 惩罚高度下降（负变化率）
-        # height_down_penalty = -5.0 * max(0, -height_rate)  # 系数可调，-height_rate 为正表示下降幅度
-
-        # # 在 compute_reward 中
-        # if self.step_counter > 50:
-        #     avg_prev_action = np.mean(self.prev_action_history, axis=0)
-        #     novelty = float(np.linalg.norm(action - avg_prev_action))
-        #     exploration_bonus = 0.05 * novelty
-        # else:
-        #     exploration_bonus = 0
-
-        # self.prev_action_history[self.history_idx] = action
-        # self.history_idx = (self.history_idx + 1) % 50
-
-
-        total = (
-            # progress_reward  + 
-                 alive_bonus + 
-                 # lateral_penalty +
-                 # action_penalty +
-                smoothness_penalty +
-                 posture_penalty
-                 + ang_vel_penalty
-                 + height_penalty
-                #  + exploration_bonus
-                # + height_down_penalty
-                 )
-        if time.time() - self.start_time >= 1200:
-            self.start_time = time.time()
-            print(
-                #   f"progress_reward:{progress_reward:.4f}",
-                #   f"lateral_penalty:{lateral_penalty:.4f}",
-                #   f"action_penalty:{action_penalty:.4f}"s, 
-                  f"height_penalty:{height_penalty:.4f}",
-                  f"smoothness_penalty:{smoothness_penalty:.4f},",
-                  f"posture_penalty:{posture_penalty:.4f}",
-                #   f"ang_vel_penalty:{ang_vel_penalty:.4f}",
-                #   f"height_down_penalty:{height_down_penalty:.4f}",
-                #   f"exploration_bonus:{exploration_bonus:.4f}"
-            )
-
-        return total
-
-
-
-    def step(self, action):
-
-        r = self.Player.robot
-        self.previous_action = action
-
-        self.target_joint_positions = (
-                # self.joint_nominal_position +
-                 self.scaling_factor * action
-        )
-        self.target_joint_positions *= self.train_sim_flip
-
-        for idx, target in enumerate(self.target_joint_positions):
-            r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=40, kd=1.0
-            )
-
-        self.previous_action = action
-
-        self.sync()  # run simulation step
-        self.step_counter += 1
-
-        if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
-            self.debug_joint_status()
-
-        current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
-
-        # Compute reward based on movement from previous step
-        reward = self.compute_reward(self.previous_pos, current_pos, action)
-
-        # Update previous position
-        self.previous_pos = current_pos.copy()
-        self.last_action_for_reward = action.copy()
-
-        # Fall detection and penalty
-        is_fallen = self.Player.world.global_position[2] < 0.3
-
-        # terminal state: the robot is falling or timeout
-        terminated = is_fallen or self.step_counter > 800 or self.route_completed
-        truncated = False
-
-        return self.observe(), reward, terminated, truncated, {}
-
-
-class Train(Train_Base):
-    def __init__(self, script) -> None:
-        super().__init__(script)
-
-    def train(self, args):
-
-        # --------------------------------------- Learning parameters
-        n_envs = int(os.environ.get("GYM_CPU_N_ENVS", "20"))
-        if n_envs < 1:
-            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
-        server_warmup_sec = float(os.environ.get("GYM_CPU_SERVER_WARMUP_SEC", "3.0"))
-        n_steps_per_env = int(os.environ.get("GYM_CPU_TRAIN_STEPS_PER_ENV", "256"))  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = int(os.environ.get("GYM_CPU_TRAIN_BATCH_SIZE", "512"))  # should be a factor of (n_steps_per_env * n_envs)
-        total_steps = 30000000
-        learning_rate = float(os.environ.get("GYM_CPU_TRAIN_LR", "3e-4"))
-        folder_name = f'Walk_R{self.robot_type}'
-        model_path = f'./scripts/gyms/logs/{folder_name}/'
-
-        print(f"Model path: {model_path}")
-        print(f"Using {n_envs} parallel environments")
-
-        # --------------------------------------- Run algorithm
-        def init_env(i_env, monitor=False):
-            def thunk():
-                env = WalkEnv(self.ip, self.server_p + i_env)
-                if monitor:
-                    env = Monitor(env)
-                return env
-
-            return thunk
-
-        server_log_dir = os.path.join(model_path, "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1, no_render=True, no_realtime=True)  # include 1 extra server for testing
-
-        # Wait for servers to start
-        print(f"Starting {n_envs + 1} rcssservermj servers...")
-        if server_warmup_sec > 0:
-            print(f"Waiting {server_warmup_sec:.1f}s for server warmup...")
-            sleep(server_warmup_sec)
-        print("Servers started, creating environments...")
-
-        env = SubprocVecEnv([init_env(i, monitor=True) for i in range(n_envs)])
-        # Use single-process eval env to avoid extra subprocess fragility during callback evaluation.
-        eval_env = DummyVecEnv([init_env(n_envs, monitor=True)])
-
-        try:
-            # Custom policy network architecture
-            policy_kwargs = dict(
-                net_arch=dict(
-                    pi=[512, 256, 128],  # Policy network: 3 layers
-                    vf=[512, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
-            )
-
-            if "model_file" in args:  # retrain
-                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
-                                 batch_size=minibatch_size, learning_rate=learning_rate)
-            else:  # train new model
-                model = PPO(
-                    "MlpPolicy",
-                    env=env,
-                    verbose=1,
-                    n_steps=n_steps_per_env,
-                    batch_size=minibatch_size,
-                    learning_rate=learning_rate,
-                    device="cpu",
-                    policy_kwargs=policy_kwargs,
-                    ent_coef=float(os.environ.get("GYM_CPU_TRAIN_ENT_COEF", "0.05")),  # Entropy coefficient for exploration
-                    clip_range=float(os.environ.get("GYM_CPU_TRAIN_CLIP_RANGE", "0.2")),  # PPO clipping parameter
-                    gae_lambda=0.95,  # GAE lambda
-                    gamma=float(os.environ.get("GYM_CPU_TRAIN_GAMMA", "0.95")), # Discount factor
-                    target_kl=0.03,
-                    n_epochs=int(os.environ.get("GYM_CPU_TRAIN_EPOCHS", "5")),
-                    # tensorboard_log=f"./scripts/gyms/logs/{folder_name}/tensorboard/"
-                )
-
-            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
-                                          eval_freq=n_steps_per_env * 20, save_freq=n_steps_per_env * 20, eval_eps=100,
-                                          backup_env_file=__file__)
-        except KeyboardInterrupt:
-            sleep(1)  # wait for child processes
-            print("\nctrl+c pressed, aborting...\n")
-            servers.kill()
-            return
-
-        env.close()
-        eval_env.close()
-        servers.kill()
-
-    def test(self, args):
-
-        # Uses different server and monitor ports
-        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        test_no_render = os.environ.get("GYM_CPU_TEST_NO_RENDER", "0") == "1"
-        test_no_realtime = os.environ.get("GYM_CPU_TEST_NO_REALTIME", "0") == "1"
-
-        server = Train_Server(
-            self.server_p - 1,
-            self.monitor_p,
-            1,
-            no_render=test_no_render,
-            no_realtime=test_no_realtime,
-        )
-        env = WalkEnv(self.ip, self.server_p - 1)
-        model = PPO.load(args["model_file"], env=env)
-
-        try:
-            self.export_model(args["model_file"], args["model_file"] + ".pkl",
-                              False)  # Export to pkl to create custom behavior
-            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
-        except KeyboardInterrupt:
-            print()
-
-        env.close()
-        server.kill()
-
-
-if __name__ == "__main__":
-    from types import SimpleNamespace
-
-    # 创建默认参数
-    script_args = SimpleNamespace(
-        args=SimpleNamespace(
-            i='127.0.0.1',  # Server IP
-            p=3100,  # Server port
-            m=3200,  # Monitor port
-            r=0,  # Robot type
-            t='Gym',  # Team name
-            u=1  # Uniform number
-        )
-    )
-
-    trainer = Train(script_args)
-
-    run_mode = os.environ.get("GYM_CPU_MODE", "train").strip().lower()
-
-    if run_mode == "test":
-        test_model_file = os.environ.get("GYM_CPU_TEST_MODEL", "scripts/gyms/logs/Walk_R0_004/best_model.zip")
-        test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Walk_R0_004/")
-        trainer.test({"model_file": test_model_file, "folder_dir": test_folder})
-    else:
-        retrain_model = os.environ.get("GYM_CPU_TRAIN_MODEL", "").strip()
-        if retrain_model:
-            trainer.train({"model_file": retrain_model})
-        else:
-            trainer.train({})

scripts/gyms/logs/Walk_R0_008/Walk.py

@@ -1,704 +0,0 @@
-import os
-import numpy as np
-import math
-import time
-from time import sleep
-from random import random
-from random import uniform
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.monitor import Monitor
-from stable_baselines3.common.vec_env import SubprocVecEnv, DummyVecEnv
-
-import gymnasium as gym
-from gymnasium import spaces
-
-from scripts.commons.Train_Base import Train_Base
-from scripts.commons.Server import Server as Train_Server
-
-from agent.base_agent import Base_Agent
-from utils.math_ops import MathOps
-
-from scipy.spatial.transform import Rotation as R
-
-'''
-Objective:
-Learn how to run forward using step primitive
-----------
-- class Basic_Run: implements an OpenAI custom gym
-- class Train:  implements algorithms to train a new model or test an existing model
-'''
-
-
-class WalkEnv(gym.Env):
-    def __init__(self, ip, server_p) -> None:
-
-        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
-        self.Player = player = Base_Agent(
-            team_name="Gym",
-            number=1,
-            host=ip,
-            port=server_p
-        )
-        self.robot_type = self.Player.robot
-        self.step_counter = 0  # to limit episode size
-        self.force_play_on = True
-
-        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
-        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
-        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
-        self.isfallen = False
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.debug_every_n_steps = 5
-        self.enable_debug_joint_status = False
-        self.calibrate_nominal_from_neutral = True
-        self.auto_calibrate_train_sim_flip = True
-        self.nominal_calibrated_once = False
-        self.flip_calibrated_once = False
-        self._target_hz = 0.0
-        self._target_dt = 0.0
-        self._last_sync_time = None
-        target_hz_env = 0
-        if target_hz_env:
-            try:
-                self._target_hz = float(target_hz_env)
-            except ValueError:
-                self._target_hz = 0.0
-        if self._target_hz > 0.0:
-            self._target_dt = 1.0 / self._target_hz
-
-        # State space
-        # 原始观测大小: 78
-        obs_size = 78
-        self.obs = np.zeros(obs_size, np.float32)
-        self.observation_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(obs_size,),
-            dtype=np.float32
-        )
-
-        action_dim = len(self.Player.robot.ROBOT_MOTORS)
-        self.no_of_actions = action_dim
-        self.action_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(action_dim,),
-            dtype=np.float32
-        )
-
-        # 中立姿态
-        self.joint_nominal_position = np.array(
-            [
-                0.0,
-                0.0,
-                0.0,
-                1.4,
-                0.0,
-                -0.4,
-                0.0,
-                -1.4,
-                0.0,
-                0.4,
-                0.0,
-                -0.4,
-                0.0,
-                0.0,
-                0.8,
-                -0.4,
-                0.0,
-                0.4,
-                0.0,
-                0.0,
-                -0.8,
-                0.4,
-                0.0,
-            ]
-        )
-        self.joint_nominal_position = np.zeros(self.no_of_actions)
-        self.train_sim_flip = np.array(
-            [
-                1.0,  # 0: Head_yaw (he1)
-                -1.0,  # 1: Head_pitch (he2)
-                1.0,  # 2: Left_Shoulder_Pitch (lae1)
-                -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                -1.0,  # 4: Left_Elbow_Pitch (lae3)
-                1.0,  # 5: Left_Elbow_Yaw (lae4)
-                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                -1.0,  # 7: Right_Shoulder_Roll (rae2)
-                1.0,  # 8: Right_Elbow_Pitch (rae3)
-                1.0,  # 9: Right_Elbow_Yaw (rae4)
-                1.0,  # 10: Waist (te1)
-                1.0,  # 11: Left_Hip_Pitch (lle1)
-                -1.0,  # 12: Left_Hip_Roll (lle2)
-                -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,  # 14: Left_Knee_Pitch (lle4)
-                1.0,  # 15: Left_Ankle_Pitch (lle5)
-                -1.0,  # 16: Left_Ankle_Roll (lle6)
-                -1.0,  # 17: Right_Hip_Pitch (rle1)
-                -1.0,  # 18: Right_Hip_Roll (rle2)
-                -1.0,  # 19: Right_Hip_Yaw (rle3)
-                -1.0,  # 20: Right_Knee_Pitch (rle4)
-                -1.0,  # 21: Right_Ankle_Pitch (rle5)
-                -1.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-
-        self.scaling_factor = 0.3
-        # self.scaling_factor = 1
-
-        # Encourage a minimum lateral stance so the policy avoids feet overlap.
-        self.min_stance_rad = 0.10
-
-        # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = False
-        self.reset_beam_yaw_range_deg = 180  # randomize target direction fully to encourage learning a real walk instead of a fixed gait
-        self.reset_joint_noise_rad = 0.015
-        self.reset_perturb_steps = 3
-        self.reset_recover_steps = 8
-
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Track previous position
-        self.Player.server.connect()
-        # sleep(2.0)  # Longer wait for connection to establish completely
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-        self.start_time = time.time()
-
-    def _reconnect_server(self):
-        try:
-            self.Player.server.shutdown()
-        except Exception:
-            pass
-
-        self.Player.server.connect()
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-
-    def _safe_receive_world_update(self, retries=1):
-        last_exc = None
-        for attempt in range(retries + 1):
-            try:
-                self.Player.server.receive()
-                self.Player.world.update()
-                return
-            except (ConnectionResetError, OSError) as exc:
-                last_exc = exc
-                if attempt >= retries:
-                    raise
-                self._reconnect_server()
-        if last_exc is not None:
-            raise last_exc
-
-    def debug_log(self, message):
-        print(message)
-        try:
-            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
-            with open(log_path, "a", encoding="utf-8") as f:
-                f.write(message + "\n")
-        except OSError:
-            pass
-
-    def observe(self, init=False):
-
-        """获取当前观测值"""
-        robot = self.Player.robot
-        world = self.Player.world
-
-        # Safety check: ensure data is available
-
-        # 计算目标速度
-        raw_target = self.target_position - world.global_position[:2]
-        velocity = MathOps.rotate_2d_vec(
-            raw_target,
-            -robot.global_orientation_euler[2],
-            is_rad=False
-        )
-
-        # 计算相对方向
-        rel_orientation = MathOps.vector_angle(velocity) * 0.3
-        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
-
-        velocity = np.concatenate([velocity, np.array([rel_orientation])])
-        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
-        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
-
-        # 关节状态
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        qpos_qvel_previous_action = np.concatenate([
-            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
-            radian_joint_speeds / 110.0 * self.train_sim_flip,
-            self.previous_action / 10.0,
-        ])
-
-        # 角速度
-        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
-
-        # 投影的重力方向
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-
-        # 组合观测
-        observation = np.concatenate([
-            qpos_qvel_previous_action,
-            ang_vel,
-            velocity,
-            projected_gravity,
-        ])
-
-        observation = np.clip(observation, -10.0, 10.0)
-        return observation.astype(np.float32)
-
-    def sync(self):
-        ''' Run a single simulation step '''
-        self._safe_receive_world_update(retries=1)
-        self.Player.robot.commit_motor_targets_pd()
-        self.Player.server.send()
-        if self._target_dt > 0.0:
-            now = time.time()
-            if self._last_sync_time is None:
-                self._last_sync_time = now
-                return
-            elapsed = now - self._last_sync_time
-            remaining = self._target_dt - elapsed
-            if remaining > 0.0:
-                time.sleep(remaining)
-                now = time.time()
-            self._last_sync_time = now
-
-    def debug_joint_status(self):
-        robot = self.Player.robot
-        actual_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        target_joint_positions = getattr(
-            self,
-            'target_joint_positions',
-            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
-        )
-        joint_error = actual_joint_positions - target_joint_positions
-        leg_slice = slice(11, None)
-
-        self.debug_log(
-            "[WalkDebug] "
-            f"step={self.step_counter} "
-            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
-            f"target_xy={np.round(self.target_position, 3).tolist()} "
-            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
-            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
-            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
-            f"fallen={self.Player.world.global_position[2] < 0.3}"
-        )
-        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
-
-    def reset(self, seed=None, options=None):
-        '''
-        Reset and stabilize the robot
-        Note: for some behaviors it would be better to reduce stabilization or add noise
-        '''
-        r = self.Player.robot
-        super().reset(seed=seed)
-        if seed is not None:
-            np.random.seed(seed)
-
-        length1 = 2  # randomize target distance
-        length2 = np.random.uniform(0.6, 1)  # randomize target distance
-        length3 = np.random.uniform(0.6, 1)  # randomize target distance
-        angle2 = np.random.uniform(-30, 30)  # randomize initial orientation
-        angle3 = np.random.uniform(-30, 30)  # randomize target direction
-
-        self.step_counter = 0
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
-        self.walk_cycle_step = 0
-
-        # 随机 beam 目标位置和朝向，增加训练多样性
-        beam_x = (random() - 0.5) * 10
-        beam_y = (random() - 0.5) * 10
-        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
-
-        for _ in range(5):
-            self._safe_receive_world_update(retries=2)
-            self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
-            self.Player.server.send()
-
-        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
-        finished_count = 0
-        for _ in range(50):
-            finished = self.Player.skills_manager.execute("Neutral")
-            self.sync()
-            if finished:
-                finished_count += 1
-                if finished_count >= 20:  # 假设需要连续20次完成才算成功
-                    break
-
-        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-            # Perturb waist + lower body only (10:), keep head/arms stable.
-            perturb_action[10:] = np.random.uniform(
-                -self.reset_joint_noise_rad,
-                self.reset_joint_noise_rad,
-                size=(self.no_of_actions - 10,)
-            )
-
-            for _ in range(self.reset_perturb_steps):
-                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-            for i in range(self.reset_recover_steps):
-                # Linearly fade perturbation to help policy start from near-neutral.
-                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-        # memory variables
-        self.sync()  
-        self.initial_position = np.array(self.Player.world.global_position[:2])
-        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions, np.float32)
-        # Build target in the robot's current forward direction instead of fixed global +x.
-        heading_deg = float(r.global_orientation_euler[2])
-        forward_offset = MathOps.rotate_2d_vec(np.array([length1, 0.0]), heading_deg, is_rad=False)
-        point1 = self.initial_position + forward_offset
-        point2 = point1 + MathOps.rotate_2d_vec(np.array([length2, 0]), angle2, is_rad=False)
-        point3 = point2 + MathOps.rotate_2d_vec(np.array([length3, 0]), angle3, is_rad=False)
-        self.point_list = [point1]
-        self.target_position = self.point_list[self.waypoint_index]
-        self.initial_height = self.Player.world.global_position[2]
-
-        return self.observe(True), {}
-
-    def render(self, mode='human', close=False):
-        return
-
-    def compute_reward(self, previous_pos, current_pos, action):
-        height = float(self.Player.world.global_position[2])
-        robot = self.Player.robot
-
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-        tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        ang_vel = np.deg2rad(robot.gyroscope)
-        ang_vel_mag = float(np.linalg.norm(ang_vel))
-
-        is_fallen = height < 0.3
-        if is_fallen:
-            # remain = max(0, 800 - self.step_counter)
-            # return -8.0 - 0.01 * remain
-            return -1.0
-        
-
-
-        # # 目标方向
-        # to_target = self.target_position - current_pos
-        # dist_to_target = float(np.linalg.norm(to_target))
-        # if dist_to_target < 0.5:
-        #     return 15.0
-
-        # forward_dir = to_target / dist_to_target if dist_to_target > 0.1 else np.array([1.0, 0.0])
-        # delta_pos = current_pos - previous_pos
-        # forward_step = float(np.dot(delta_pos, forward_dir))
-        # lateral_step = float(np.linalg.norm(delta_pos - forward_dir * forward_step))
-
-        # 奖励项
-        # progress_reward = 2 * forward_step
-        # lateral_penalty = -0.1 * lateral_step
-        alive_bonus = 2.0
-
-        # action_penalty = -0.01 * float(np.linalg.norm(action))
-        smoothness_penalty = -0.01 * float(np.linalg.norm(action - self.last_action_for_reward))
-
-        posture_penalty = -0.3 * (tilt_mag)
-        ang_vel_penalty = -0.02 * ang_vel_mag
-
-        # Use simulator joint readings in training frame to shape lateral stance.
-        joint_pos = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        ) * self.train_sim_flip
-        left_hip_roll = float(joint_pos[12])
-        right_hip_roll = float(joint_pos[18])
-        left_ankle_roll = float(joint_pos[16])
-        right_ankle_roll = float(joint_pos[22])
-
-        hip_spread = left_hip_roll - right_hip_roll
-        ankle_spread = left_ankle_roll - right_ankle_roll
-        stance_metric = 0.6 * abs(hip_spread) + 0.4 * abs(ankle_spread)
-
-        # Penalize narrow stance (feet too close) and scissoring (cross-leg pattern).
-        stance_collapse_penalty = -4.0 * max(0.0, self.min_stance_rad - stance_metric)
-        cross_leg_penalty = -1.2 * max(0.0, -(hip_spread * ankle_spread))
-
-        target_height = self.initial_height
-        height_error =  height - target_height
-        height_penalty = -0.5 * abs(height_error)  # 惩罚高度偏离，系数可调
-
-        # # 在 compute_reward 开头附近，添加高度变化率计算
-        # if not hasattr(self, 'last_height'):
-        #     self.last_height = height
-        #     self.last_height_time = self.step_counter  # 可选，用于时间间隔
-        # height_rate = height - self.last_height  # 正为上升，负为下降
-        # self.last_height = height
-
-        # 惩罚高度下降（负变化率）
-        # height_down_penalty = -5.0 * max(0, -height_rate)  # 系数可调，-height_rate 为正表示下降幅度
-
-        # # 在 compute_reward 中
-        # if self.step_counter > 50:
-        #     avg_prev_action = np.mean(self.prev_action_history, axis=0)
-        #     novelty = float(np.linalg.norm(action - avg_prev_action))
-        #     exploration_bonus = 0.05 * novelty
-        # else:
-        #     exploration_bonus = 0
-
-        # self.prev_action_history[self.history_idx] = action
-        # self.history_idx = (self.history_idx + 1) % 50
-
-
-        total = (
-            # progress_reward  + 
-                 alive_bonus + 
-                 # lateral_penalty +
-                 # action_penalty +
-                smoothness_penalty +
-                 posture_penalty
-                 + ang_vel_penalty
-                 + height_penalty
-                 + stance_collapse_penalty
-                 + cross_leg_penalty
-                #  + exploration_bonus
-                # + height_down_penalty
-                 )
-        if time.time() - self.start_time >= 600:
-            self.start_time = time.time()
-            print(
-                #   f"progress_reward:{progress_reward:.4f}",
-                #   f"lateral_penalty:{lateral_penalty:.4f}",
-                #   f"action_penalty:{action_penalty:.4f}"s, 
-                  f"height_penalty:{height_penalty:.4f}",
-                  f"smoothness_penalty:{smoothness_penalty:.4f},",
-                  f"posture_penalty:{posture_penalty:.4f}",
-                  f"stance_collapse_penalty:{stance_collapse_penalty:.4f}",
-                  f"cross_leg_penalty:{cross_leg_penalty:.4f}",
-                #   f"ang_vel_penalty:{ang_vel_penalty:.4f}",
-                #   f"height_down_penalty:{height_down_penalty:.4f}",
-                #   f"exploration_bonus:{exploration_bonus:.4f}"
-            )
-
-        return total
-
-
-
-    def step(self, action):
-
-        r = self.Player.robot
-        self.previous_action = action
-
-        self.target_joint_positions = (
-                # self.joint_nominal_position +
-                 self.scaling_factor * action
-        )
-        self.target_joint_positions *= self.train_sim_flip
-
-        for idx, target in enumerate(self.target_joint_positions):
-            r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=40, kd=1.0
-            )
-
-        self.previous_action = action
-
-        self.sync()  # run simulation step
-        self.step_counter += 1
-
-        if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
-            self.debug_joint_status()
-
-        current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
-
-        # Compute reward based on movement from previous step
-        reward = self.compute_reward(self.previous_pos, current_pos, action)
-
-        # Update previous position
-        self.previous_pos = current_pos.copy()
-        self.last_action_for_reward = action.copy()
-
-        # Fall detection and penalty
-        is_fallen = self.Player.world.global_position[2] < 0.3
-
-        # terminal state: the robot is falling or timeout
-        terminated = is_fallen or self.step_counter > 800 or self.route_completed
-        truncated = False
-
-        return self.observe(), reward, terminated, truncated, {}
-
-
-class Train(Train_Base):
-    def __init__(self, script) -> None:
-        super().__init__(script)
-
-    def train(self, args):
-
-        # --------------------------------------- Learning parameters
-        n_envs = int(os.environ.get("GYM_CPU_N_ENVS", "20"))
-        if n_envs < 1:
-            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
-        server_warmup_sec = float(os.environ.get("GYM_CPU_SERVER_WARMUP_SEC", "3.0"))
-        n_steps_per_env = int(os.environ.get("GYM_CPU_TRAIN_STEPS_PER_ENV", "256"))  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = int(os.environ.get("GYM_CPU_TRAIN_BATCH_SIZE", "512"))  # should be a factor of (n_steps_per_env * n_envs)
-        total_steps = 30000000
-        learning_rate = float(os.environ.get("GYM_CPU_TRAIN_LR", "3e-4"))
-        folder_name = f'Walk_R{self.robot_type}'
-        model_path = f'./scripts/gyms/logs/{folder_name}/'
-
-        print(f"Model path: {model_path}")
-        print(f"Using {n_envs} parallel environments")
-
-        # --------------------------------------- Run algorithm
-        def init_env(i_env, monitor=False):
-            def thunk():
-                env = WalkEnv(self.ip, self.server_p + i_env)
-                if monitor:
-                    env = Monitor(env)
-                return env
-
-            return thunk
-
-        server_log_dir = os.path.join(model_path, "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1, no_render=True, no_realtime=True)  # include 1 extra server for testing
-
-        # Wait for servers to start
-        print(f"Starting {n_envs + 1} rcssservermj servers...")
-        if server_warmup_sec > 0:
-            print(f"Waiting {server_warmup_sec:.1f}s for server warmup...")
-            sleep(server_warmup_sec)
-        print("Servers started, creating environments...")
-
-        env = SubprocVecEnv([init_env(i, monitor=True) for i in range(n_envs)])
-        # Use single-process eval env to avoid extra subprocess fragility during callback evaluation.
-        eval_env = DummyVecEnv([init_env(n_envs, monitor=True)])
-
-        try:
-            # Custom policy network architecture
-            policy_kwargs = dict(
-                net_arch=dict(
-                    pi=[512, 256, 128],  # Policy network: 3 layers
-                    vf=[512, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
-            )
-
-            if "model_file" in args:  # retrain
-                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
-                                 batch_size=minibatch_size, learning_rate=learning_rate)
-            else:  # train new model
-                model = PPO(
-                    "MlpPolicy",
-                    env=env,
-                    verbose=1,
-                    n_steps=n_steps_per_env,
-                    batch_size=minibatch_size,
-                    learning_rate=learning_rate,
-                    device="cpu",
-                    policy_kwargs=policy_kwargs,
-                    ent_coef=float(os.environ.get("GYM_CPU_TRAIN_ENT_COEF", "0.05")),  # Entropy coefficient for exploration
-                    clip_range=float(os.environ.get("GYM_CPU_TRAIN_CLIP_RANGE", "0.2")),  # PPO clipping parameter
-                    gae_lambda=0.95,  # GAE lambda
-                    gamma=float(os.environ.get("GYM_CPU_TRAIN_GAMMA", "0.95")), # Discount factor
-                    target_kl=0.03,
-                    n_epochs=int(os.environ.get("GYM_CPU_TRAIN_EPOCHS", "5")),
-                    tensorboard_log=f"./scripts/gyms/logs/{folder_name}/tensorboard/"
-                )
-
-            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
-                                          eval_freq=n_steps_per_env * 20, save_freq=n_steps_per_env * 20, eval_eps=100,
-                                          backup_env_file=__file__)
-        except KeyboardInterrupt:
-            sleep(1)  # wait for child processes
-            print("\nctrl+c pressed, aborting...\n")
-            servers.kill()
-            return
-
-        env.close()
-        eval_env.close()
-        servers.kill()
-
-    def test(self, args):
-
-        # Uses different server and monitor ports
-        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        test_no_render = os.environ.get("GYM_CPU_TEST_NO_RENDER", "0") == "1"
-        test_no_realtime = os.environ.get("GYM_CPU_TEST_NO_REALTIME", "0") == "1"
-
-        server = Train_Server(
-            self.server_p - 1,
-            self.monitor_p,
-            1,
-            no_render=test_no_render,
-            no_realtime=test_no_realtime,
-        )
-        env = WalkEnv(self.ip, self.server_p - 1)
-        model = PPO.load(args["model_file"], env=env)
-
-        try:
-            self.export_model(args["model_file"], args["model_file"] + ".pkl",
-                              False)  # Export to pkl to create custom behavior
-            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
-        except KeyboardInterrupt:
-            print()
-
-        env.close()
-        server.kill()
-
-
-if __name__ == "__main__":
-    from types import SimpleNamespace
-
-    # 创建默认参数
-    script_args = SimpleNamespace(
-        args=SimpleNamespace(
-            i='127.0.0.1',  # Server IP
-            p=3100,  # Server port
-            m=3200,  # Monitor port
-            r=0,  # Robot type
-            t='Gym',  # Team name
-            u=1  # Uniform number
-        )
-    )
-
-    trainer = Train(script_args)
-
-    run_mode = os.environ.get("GYM_CPU_MODE", "train").strip().lower()
-
-    if run_mode == "test":
-        test_model_file = os.environ.get("GYM_CPU_TEST_MODEL", "scripts/gyms/logs/Walk_R0_004/best_model.zip")
-        test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Walk_R0_004/")
-        trainer.test({"model_file": test_model_file, "folder_dir": test_folder})
-    else:
-        retrain_model = os.environ.get("GYM_CPU_TRAIN_MODEL", "").strip()
-        if retrain_model:
-            trainer.train({"model_file": retrain_model})
-        else:
-            trainer.train({})

scripts/gyms/logs/Walk_R0_009/Walk.py

@@ -1,705 +0,0 @@
-import os
-import numpy as np
-import math
-import time
-from time import sleep
-from random import random
-from random import uniform
-from itertools import count
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.monitor import Monitor
-from stable_baselines3.common.vec_env import SubprocVecEnv, DummyVecEnv
-
-import gymnasium as gym
-from gymnasium import spaces
-
-from scripts.commons.Train_Base import Train_Base
-from scripts.commons.Server import Server as Train_Server
-
-from agent.base_agent import Base_Agent
-from utils.math_ops import MathOps
-
-from scipy.spatial.transform import Rotation as R
-
-'''
-Objective:
-Learn how to run forward using step primitive
-----------
-- class Basic_Run: implements an OpenAI custom gym
-- class Train:  implements algorithms to train a new model or test an existing model
-'''
-
-
-class WalkEnv(gym.Env):
-    def __init__(self, ip, server_p) -> None:
-
-        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
-        self.Player = player = Base_Agent(
-            team_name="Gym",
-            number=1,
-            host=ip,
-            port=server_p
-        )
-        self.robot_type = self.Player.robot
-        self.step_counter = 0  # to limit episode size
-        self.force_play_on = True
-
-        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
-        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
-        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
-        self.isfallen = False
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.debug_every_n_steps = 5
-        self.enable_debug_joint_status = False
-        self.calibrate_nominal_from_neutral = True
-        self.auto_calibrate_train_sim_flip = True
-        self.nominal_calibrated_once = False
-        self.flip_calibrated_once = False
-        self._target_hz = 0.0
-        self._target_dt = 0.0
-        self._last_sync_time = None
-        target_hz_env = 0
-        if target_hz_env:
-            try:
-                self._target_hz = float(target_hz_env)
-            except ValueError:
-                self._target_hz = 0.0
-        if self._target_hz > 0.0:
-            self._target_dt = 1.0 / self._target_hz
-
-        # State space
-        # 原始观测大小: 78
-        obs_size = 78
-        self.obs = np.zeros(obs_size, np.float32)
-        self.observation_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(obs_size,),
-            dtype=np.float32
-        )
-
-        action_dim = len(self.Player.robot.ROBOT_MOTORS)
-        self.no_of_actions = action_dim
-        self.action_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(action_dim,),
-            dtype=np.float32
-        )
-
-        # 中立姿态
-        self.joint_nominal_position = np.array(
-            [
-                0.0,
-                0.0,
-                0.0,
-                1.4,
-                0.0,
-                -0.4,
-                0.0,
-                -1.4,
-                0.0,
-                0.4,
-                0.0,
-                -0.4,
-                0.0,
-                0.0,
-                0.8,
-                -0.4,
-                0.0,
-                0.4,
-                0.0,
-                0.0,
-                -0.8,
-                0.4,
-                0.0,
-            ]
-        )
-        self.joint_nominal_position = np.zeros(self.no_of_actions)
-        self.train_sim_flip = np.array(
-            [
-                1.0,  # 0: Head_yaw (he1)
-                -1.0,  # 1: Head_pitch (he2)
-                1.0,  # 2: Left_Shoulder_Pitch (lae1)
-                -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                -1.0,  # 4: Left_Elbow_Pitch (lae3)
-                1.0,  # 5: Left_Elbow_Yaw (lae4)
-                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                -1.0,  # 7: Right_Shoulder_Roll (rae2)
-                1.0,  # 8: Right_Elbow_Pitch (rae3)
-                1.0,  # 9: Right_Elbow_Yaw (rae4)
-                1.0,  # 10: Waist (te1)
-                1.0,  # 11: Left_Hip_Pitch (lle1)
-                -1.0,  # 12: Left_Hip_Roll (lle2)
-                -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,  # 14: Left_Knee_Pitch (lle4)
-                1.0,  # 15: Left_Ankle_Pitch (lle5)
-                -1.0,  # 16: Left_Ankle_Roll (lle6)
-                -1.0,  # 17: Right_Hip_Pitch (rle1)
-                -1.0,  # 18: Right_Hip_Roll (rle2)
-                -1.0,  # 19: Right_Hip_Yaw (rle3)
-                -1.0,  # 20: Right_Knee_Pitch (rle4)
-                -1.0,  # 21: Right_Ankle_Pitch (rle5)
-                -1.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-
-        self.scaling_factor = 0.3
-        # self.scaling_factor = 1
-
-        # Encourage a minimum lateral stance so the policy avoids feet overlap.
-        self.min_stance_rad = 0.10
-
-        # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = False
-        self.reset_beam_yaw_range_deg = 180  # randomize target direction fully to encourage learning a real walk instead of a fixed gait
-        self.reset_joint_noise_rad = 0.015
-        self.reset_perturb_steps = 3
-        self.reset_recover_steps = 8
-
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Track previous position
-        self.Player.server.connect()
-        # sleep(2.0)  # Longer wait for connection to establish completely
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-        self.start_time = time.time()
-
-    def _reconnect_server(self):
-        try:
-            self.Player.server.shutdown()
-        except Exception:
-            pass
-
-        self.Player.server.connect()
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-
-    def _safe_receive_world_update(self, retries=1):
-        last_exc = None
-        for attempt in range(retries + 1):
-            try:
-                self.Player.server.receive()
-                self.Player.world.update()
-                return
-            except (ConnectionResetError, OSError) as exc:
-                last_exc = exc
-                if attempt >= retries:
-                    raise
-                self._reconnect_server()
-        if last_exc is not None:
-            raise last_exc
-
-    def debug_log(self, message):
-        print(message)
-        try:
-            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
-            with open(log_path, "a", encoding="utf-8") as f:
-                f.write(message + "\n")
-        except OSError:
-            pass
-
-    def observe(self, init=False):
-
-        """获取当前观测值"""
-        robot = self.Player.robot
-        world = self.Player.world
-
-        # Safety check: ensure data is available
-
-        # 计算目标速度
-        raw_target = self.target_position - world.global_position[:2]
-        velocity = MathOps.rotate_2d_vec(
-            raw_target,
-            -robot.global_orientation_euler[2],
-            is_rad=False
-        )
-
-        # 计算相对方向
-        rel_orientation = MathOps.vector_angle(velocity) * 0.3
-        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
-
-        velocity = np.concatenate([velocity, np.array([rel_orientation])])
-        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
-        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
-
-        # 关节状态
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        qpos_qvel_previous_action = np.concatenate([
-            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
-            radian_joint_speeds / 110.0 * self.train_sim_flip,
-            self.previous_action / 10.0,
-        ])
-
-        # 角速度
-        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
-
-        # 投影的重力方向
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-
-        # 组合观测
-        observation = np.concatenate([
-            qpos_qvel_previous_action,
-            ang_vel,
-            velocity,
-            projected_gravity,
-        ])
-
-        observation = np.clip(observation, -10.0, 10.0)
-        return observation.astype(np.float32)
-
-    def sync(self):
-        ''' Run a single simulation step '''
-        self._safe_receive_world_update(retries=1)
-        self.Player.robot.commit_motor_targets_pd()
-        self.Player.server.send()
-        if self._target_dt > 0.0:
-            now = time.time()
-            if self._last_sync_time is None:
-                self._last_sync_time = now
-                return
-            elapsed = now - self._last_sync_time
-            remaining = self._target_dt - elapsed
-            if remaining > 0.0:
-                time.sleep(remaining)
-                now = time.time()
-            self._last_sync_time = now
-
-    def debug_joint_status(self):
-        robot = self.Player.robot
-        actual_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        target_joint_positions = getattr(
-            self,
-            'target_joint_positions',
-            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
-        )
-        joint_error = actual_joint_positions - target_joint_positions
-        leg_slice = slice(11, None)
-
-        self.debug_log(
-            "[WalkDebug] "
-            f"step={self.step_counter} "
-            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
-            f"target_xy={np.round(self.target_position, 3).tolist()} "
-            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
-            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
-            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
-            f"fallen={self.Player.world.global_position[2] < 0.3}"
-        )
-        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
-
-    def reset(self, seed=None, options=None):
-        '''
-        Reset and stabilize the robot
-        Note: for some behaviors it would be better to reduce stabilization or add noise
-        '''
-        r = self.Player.robot
-        super().reset(seed=seed)
-        if seed is not None:
-            np.random.seed(seed)
-
-        length1 = 2  # randomize target distance
-        length2 = np.random.uniform(0.6, 1)  # randomize target distance
-        length3 = np.random.uniform(0.6, 1)  # randomize target distance
-        angle2 = np.random.uniform(-30, 30)  # randomize initial orientation
-        angle3 = np.random.uniform(-30, 30)  # randomize target direction
-
-        self.step_counter = 0
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
-        self.walk_cycle_step = 0
-
-        # 随机 beam 目标位置和朝向，增加训练多样性
-        beam_x = (random() - 0.5) * 10
-        beam_y = (random() - 0.5) * 10
-        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
-
-        for _ in range(5):
-            self._safe_receive_world_update(retries=2)
-            self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
-            self.Player.server.send()
-
-        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
-        finished_count = 0
-        for _ in range(50):
-            finished = self.Player.skills_manager.execute("Neutral")
-            self.sync()
-            if finished:
-                finished_count += 1
-                if finished_count >= 20:  # 假设需要连续20次完成才算成功
-                    break
-
-        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-            # Perturb waist + lower body only (10:), keep head/arms stable.
-            perturb_action[10:] = np.random.uniform(
-                -self.reset_joint_noise_rad,
-                self.reset_joint_noise_rad,
-                size=(self.no_of_actions - 10,)
-            )
-
-            for _ in range(self.reset_perturb_steps):
-                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-            for i in range(self.reset_recover_steps):
-                # Linearly fade perturbation to help policy start from near-neutral.
-                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-        # memory variables
-        self.sync()  
-        self.initial_position = np.array(self.Player.world.global_position[:2])
-        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions, np.float32)
-        # Build target in the robot's current forward direction instead of fixed global +x.
-        heading_deg = float(r.global_orientation_euler[2])
-        forward_offset = MathOps.rotate_2d_vec(np.array([length1, 0.0]), heading_deg, is_rad=False)
-        point1 = self.initial_position + forward_offset
-        point2 = point1 + MathOps.rotate_2d_vec(np.array([length2, 0]), angle2, is_rad=False)
-        point3 = point2 + MathOps.rotate_2d_vec(np.array([length3, 0]), angle3, is_rad=False)
-        self.point_list = [point1]
-        self.target_position = self.point_list[self.waypoint_index]
-        self.initial_height = self.Player.world.global_position[2]
-
-        return self.observe(True), {}
-
-    def render(self, mode='human', close=False):
-        return
-
-    def compute_reward(self, previous_pos, current_pos, action):
-        height = float(self.Player.world.global_position[2])
-        robot = self.Player.robot
-
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-        tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        ang_vel = np.deg2rad(robot.gyroscope)
-        ang_vel_mag = float(np.linalg.norm(ang_vel))
-
-        is_fallen = height < 0.3
-        if is_fallen:
-            # remain = max(0, 800 - self.step_counter)
-            # return -8.0 - 0.01 * remain
-            return -1.0
-        
-
-
-        # # 目标方向
-        # to_target = self.target_position - current_pos
-        # dist_to_target = float(np.linalg.norm(to_target))
-        # if dist_to_target < 0.5:
-        #     return 15.0
-
-        # forward_dir = to_target / dist_to_target if dist_to_target > 0.1 else np.array([1.0, 0.0])
-        # delta_pos = current_pos - previous_pos
-        # forward_step = float(np.dot(delta_pos, forward_dir))
-        # lateral_step = float(np.linalg.norm(delta_pos - forward_dir * forward_step))
-
-        # 奖励项
-        # progress_reward = 2 * forward_step
-        # lateral_penalty = -0.1 * lateral_step
-        alive_bonus = 2.0
-
-        # action_penalty = -0.01 * float(np.linalg.norm(action))
-        smoothness_penalty = -0.01 * float(np.linalg.norm(action - self.last_action_for_reward))
-
-        posture_penalty = -0.3 * (tilt_mag)
-        ang_vel_penalty = -0.02 * ang_vel_mag
-
-        # Use simulator joint readings in training frame to shape lateral stance.
-        joint_pos = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        ) * self.train_sim_flip
-        left_hip_roll = float(joint_pos[12])
-        right_hip_roll = float(joint_pos[18])
-        left_ankle_roll = float(joint_pos[16])
-        right_ankle_roll = float(joint_pos[22])
-
-        hip_spread = left_hip_roll - right_hip_roll
-        ankle_spread = left_ankle_roll - right_ankle_roll
-        stance_metric = 0.6 * abs(hip_spread) + 0.4 * abs(ankle_spread)
-
-        # Penalize narrow stance (feet too close) and scissoring (cross-leg pattern).
-        stance_collapse_penalty = -4.0 * max(0.0, self.min_stance_rad - stance_metric)
-        cross_leg_penalty = -1.2 * max(0.0, -(hip_spread * ankle_spread))
-
-        target_height = self.initial_height
-        height_error =  height - target_height
-        height_penalty = -0.5 * abs(height_error)  # 惩罚高度偏离，系数可调
-
-        # # 在 compute_reward 开头附近，添加高度变化率计算
-        # if not hasattr(self, 'last_height'):
-        #     self.last_height = height
-        #     self.last_height_time = self.step_counter  # 可选，用于时间间隔
-        # height_rate = height - self.last_height  # 正为上升，负为下降
-        # self.last_height = height
-
-        # 惩罚高度下降（负变化率）
-        # height_down_penalty = -5.0 * max(0, -height_rate)  # 系数可调，-height_rate 为正表示下降幅度
-
-        # # 在 compute_reward 中
-        # if self.step_counter > 50:
-        #     avg_prev_action = np.mean(self.prev_action_history, axis=0)
-        #     novelty = float(np.linalg.norm(action - avg_prev_action))
-        #     exploration_bonus = 0.05 * novelty
-        # else:
-        #     exploration_bonus = 0
-
-        # self.prev_action_history[self.history_idx] = action
-        # self.history_idx = (self.history_idx + 1) % 50
-
-
-        total = (
-            # progress_reward  + 
-                 alive_bonus + 
-                 # lateral_penalty +
-                 # action_penalty +
-                smoothness_penalty +
-                 posture_penalty
-                 + ang_vel_penalty
-                 + height_penalty
-                 + stance_collapse_penalty
-                 + cross_leg_penalty
-                #  + exploration_bonus
-                # + height_down_penalty
-                 )
-        if time.time() - self.start_time >= 600:
-            self.start_time = time.time()
-            print(
-                #   f"progress_reward:{progress_reward:.4f}",
-                #   f"lateral_penalty:{lateral_penalty:.4f}",
-                #   f"action_penalty:{action_penalty:.4f}"s, 
-                  f"height_penalty:{height_penalty:.4f}",
-                  f"smoothness_penalty:{smoothness_penalty:.4f},",
-                  f"posture_penalty:{posture_penalty:.4f}",
-                  f"stance_collapse_penalty:{stance_collapse_penalty:.4f}",
-                  f"cross_leg_penalty:{cross_leg_penalty:.4f}",
-                #   f"ang_vel_penalty:{ang_vel_penalty:.4f}",
-                #   f"height_down_penalty:{height_down_penalty:.4f}",
-                #   f"exploration_bonus:{exploration_bonus:.4f}"
-            )
-
-        return total
-
-
-
-    def step(self, action):
-
-        r = self.Player.robot
-        self.previous_action = action
-
-        self.target_joint_positions = (
-                # self.joint_nominal_position +
-                 self.scaling_factor * action
-        )
-        self.target_joint_positions *= self.train_sim_flip
-
-        for idx, target in enumerate(self.target_joint_positions):
-            r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=40, kd=1.0
-            )
-
-        self.previous_action = action
-
-        self.sync()  # run simulation step
-        self.step_counter += 1
-
-        if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
-            self.debug_joint_status()
-
-        current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
-
-        # Compute reward based on movement from previous step
-        reward = self.compute_reward(self.previous_pos, current_pos, action)
-
-        # Update previous position
-        self.previous_pos = current_pos.copy()
-        self.last_action_for_reward = action.copy()
-
-        # Fall detection and penalty
-        is_fallen = self.Player.world.global_position[2] < 0.3
-
-        # terminal state: the robot is falling or timeout
-        terminated = is_fallen or self.step_counter > 800 or self.route_completed
-        truncated = False
-
-        return self.observe(), reward, terminated, truncated, {}
-
-
-class Train(Train_Base):
-    def __init__(self, script) -> None:
-        super().__init__(script)
-
-    def train(self, args):
-
-        # --------------------------------------- Learning parameters
-        n_envs = int(os.environ.get("GYM_CPU_N_ENVS", "20"))
-        if n_envs < 1:
-            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
-        server_warmup_sec = float(os.environ.get("GYM_CPU_SERVER_WARMUP_SEC", "3.0"))
-        n_steps_per_env = int(os.environ.get("GYM_CPU_TRAIN_STEPS_PER_ENV", "256"))  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = int(os.environ.get("GYM_CPU_TRAIN_BATCH_SIZE", "512"))  # should be a factor of (n_steps_per_env * n_envs)
-        total_steps = 30000000
-        learning_rate = float(os.environ.get("GYM_CPU_TRAIN_LR", "3e-4"))
-        folder_name = f'Walk_R{self.robot_type}'
-        model_path = f'./scripts/gyms/logs/{folder_name}/'
-
-        print(f"Model path: {model_path}")
-        print(f"Using {n_envs} parallel environments")
-
-        # --------------------------------------- Run algorithm
-        def init_env(i_env, monitor=False):
-            def thunk():
-                env = WalkEnv(self.ip, self.server_p + i_env)
-                if monitor:
-                    env = Monitor(env)
-                return env
-
-            return thunk
-
-        server_log_dir = os.path.join(model_path, "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1, no_render=True, no_realtime=True)  # include 1 extra server for testing
-
-        # Wait for servers to start
-        print(f"Starting {n_envs + 1} rcssservermj servers...")
-        if server_warmup_sec > 0:
-            print(f"Waiting {server_warmup_sec:.1f}s for server warmup...")
-            sleep(server_warmup_sec)
-        print("Servers started, creating environments...")
-
-        env = SubprocVecEnv([init_env(i, monitor=True) for i in range(n_envs)])
-        # Use single-process eval env to avoid extra subprocess fragility during callback evaluation.
-        eval_env = DummyVecEnv([init_env(n_envs, monitor=True)])
-
-        try:
-            # Custom policy network architecture
-            policy_kwargs = dict(
-                net_arch=dict(
-                    pi=[512, 256, 128],  # Policy network: 3 layers
-                    vf=[512, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
-            )
-
-            if "model_file" in args:  # retrain
-                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
-                                 batch_size=minibatch_size, learning_rate=learning_rate)
-            else:  # train new model
-                model = PPO(
-                    "MlpPolicy",
-                    env=env,
-                    verbose=1,
-                    n_steps=n_steps_per_env,
-                    batch_size=minibatch_size,
-                    learning_rate=learning_rate,
-                    device="cpu",
-                    policy_kwargs=policy_kwargs,
-                    ent_coef=float(os.environ.get("GYM_CPU_TRAIN_ENT_COEF", "0.05")),  # Entropy coefficient for exploration
-                    clip_range=float(os.environ.get("GYM_CPU_TRAIN_CLIP_RANGE", "0.2")),  # PPO clipping parameter
-                    gae_lambda=0.95,  # GAE lambda
-                    gamma=float(os.environ.get("GYM_CPU_TRAIN_GAMMA", "0.95")), # Discount factor
-                    # target_kl=0.03,
-                    n_epochs=int(os.environ.get("GYM_CPU_TRAIN_EPOCHS", "5")),
-                    tensorboard_log=f"./scripts/gyms/logs/{folder_name}/tensorboard/"
-                )
-
-            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
-                                          eval_freq=n_steps_per_env * 20, save_freq=n_steps_per_env * 20, eval_eps=100,
-                                          backup_env_file=__file__)
-        except KeyboardInterrupt:
-            sleep(1)  # wait for child processes
-            print("\nctrl+c pressed, aborting...\n")
-            servers.kill()
-            return
-
-        env.close()
-        eval_env.close()
-        servers.kill()
-
-    def test(self, args):
-
-        # Uses different server and monitor ports
-        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        test_no_render = os.environ.get("GYM_CPU_TEST_NO_RENDER", "0") == "1"
-        test_no_realtime = os.environ.get("GYM_CPU_TEST_NO_REALTIME", "0") == "1"
-
-        server = Train_Server(
-            self.server_p - 1,
-            self.monitor_p,
-            1,
-            no_render=test_no_render,
-            no_realtime=test_no_realtime,
-        )
-        env = WalkEnv(self.ip, self.server_p - 1)
-        model = PPO.load(args["model_file"], env=env)
-
-        try:
-            self.export_model(args["model_file"], args["model_file"] + ".pkl",
-                              False)  # Export to pkl to create custom behavior
-            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
-        except KeyboardInterrupt:
-            print()
-
-        env.close()
-        server.kill()
-
-
-if __name__ == "__main__":
-    from types import SimpleNamespace
-
-    # 创建默认参数
-    script_args = SimpleNamespace(
-        args=SimpleNamespace(
-            i='127.0.0.1',  # Server IP
-            p=3100,  # Server port
-            m=3200,  # Monitor port
-            r=0,  # Robot type
-            t='Gym',  # Team name
-            u=1  # Uniform number
-        )
-    )
-
-    trainer = Train(script_args)
-
-    run_mode = os.environ.get("GYM_CPU_MODE", "train").strip().lower()
-
-    if run_mode == "test":
-        test_model_file = os.environ.get("GYM_CPU_TEST_MODEL", "scripts/gyms/logs/Walk_R0_004/best_model.zip")
-        test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Walk_R0_004/")
-        trainer.test({"model_file": test_model_file, "folder_dir": test_folder})
-    else:
-        retrain_model = os.environ.get("GYM_CPU_TRAIN_MODEL", "").strip()
-        if retrain_model:
-            trainer.train({"model_file": retrain_model})
-        else:
-            trainer.train({})

scripts/gyms/logs/Walk_R0_010/Walk.py

@@ -1,705 +0,0 @@
-import os
-import numpy as np
-import math
-import time
-from time import sleep
-from random import random
-from random import uniform
-from itertools import count
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.monitor import Monitor
-from stable_baselines3.common.vec_env import SubprocVecEnv, DummyVecEnv
-
-import gymnasium as gym
-from gymnasium import spaces
-
-from scripts.commons.Train_Base import Train_Base
-from scripts.commons.Server import Server as Train_Server
-
-from agent.base_agent import Base_Agent
-from utils.math_ops import MathOps
-
-from scipy.spatial.transform import Rotation as R
-
-'''
-Objective:
-Learn how to run forward using step primitive
-----------
-- class Basic_Run: implements an OpenAI custom gym
-- class Train:  implements algorithms to train a new model or test an existing model
-'''
-
-
-class WalkEnv(gym.Env):
-    def __init__(self, ip, server_p) -> None:
-
-        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
-        self.Player = player = Base_Agent(
-            team_name="Gym",
-            number=1,
-            host=ip,
-            port=server_p
-        )
-        self.robot_type = self.Player.robot
-        self.step_counter = 0  # to limit episode size
-        self.force_play_on = True
-
-        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
-        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
-        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
-        self.isfallen = False
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.debug_every_n_steps = 5
-        self.enable_debug_joint_status = False
-        self.calibrate_nominal_from_neutral = True
-        self.auto_calibrate_train_sim_flip = True
-        self.nominal_calibrated_once = False
-        self.flip_calibrated_once = False
-        self._target_hz = 0.0
-        self._target_dt = 0.0
-        self._last_sync_time = None
-        target_hz_env = 0
-        if target_hz_env:
-            try:
-                self._target_hz = float(target_hz_env)
-            except ValueError:
-                self._target_hz = 0.0
-        if self._target_hz > 0.0:
-            self._target_dt = 1.0 / self._target_hz
-
-        # State space
-        # 原始观测大小: 78
-        obs_size = 78
-        self.obs = np.zeros(obs_size, np.float32)
-        self.observation_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(obs_size,),
-            dtype=np.float32
-        )
-
-        action_dim = len(self.Player.robot.ROBOT_MOTORS)
-        self.no_of_actions = action_dim
-        self.action_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(action_dim,),
-            dtype=np.float32
-        )
-
-        # 中立姿态
-        self.joint_nominal_position = np.array(
-            [
-                0.0,
-                0.0,
-                0.0,
-                1.4,
-                0.0,
-                -0.4,
-                0.0,
-                -1.4,
-                0.0,
-                0.4,
-                0.0,
-                -0.4,
-                0.0,
-                0.0,
-                0.8,
-                -0.4,
-                0.0,
-                0.4,
-                0.0,
-                0.0,
-                -0.8,
-                0.4,
-                0.0,
-            ]
-        )
-        self.joint_nominal_position = np.zeros(self.no_of_actions)
-        self.train_sim_flip = np.array(
-            [
-                1.0,  # 0: Head_yaw (he1)
-                -1.0,  # 1: Head_pitch (he2)
-                1.0,  # 2: Left_Shoulder_Pitch (lae1)
-                -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                -1.0,  # 4: Left_Elbow_Pitch (lae3)
-                1.0,  # 5: Left_Elbow_Yaw (lae4)
-                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                -1.0,  # 7: Right_Shoulder_Roll (rae2)
-                1.0,  # 8: Right_Elbow_Pitch (rae3)
-                1.0,  # 9: Right_Elbow_Yaw (rae4)
-                1.0,  # 10: Waist (te1)
-                1.0,  # 11: Left_Hip_Pitch (lle1)
-                -1.0,  # 12: Left_Hip_Roll (lle2)
-                -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,  # 14: Left_Knee_Pitch (lle4)
-                1.0,  # 15: Left_Ankle_Pitch (lle5)
-                -1.0,  # 16: Left_Ankle_Roll (lle6)
-                -1.0,  # 17: Right_Hip_Pitch (rle1)
-                -1.0,  # 18: Right_Hip_Roll (rle2)
-                -1.0,  # 19: Right_Hip_Yaw (rle3)
-                -1.0,  # 20: Right_Knee_Pitch (rle4)
-                -1.0,  # 21: Right_Ankle_Pitch (rle5)
-                -1.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-
-        self.scaling_factor = 0.3
-        # self.scaling_factor = 1
-
-        # Encourage a minimum lateral stance so the policy avoids feet overlap.
-        self.min_stance_rad = 0.10
-
-        # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = False
-        self.reset_beam_yaw_range_deg = 180  # randomize target direction fully to encourage learning a real walk instead of a fixed gait
-        self.reset_joint_noise_rad = 0.015
-        self.reset_perturb_steps = 3
-        self.reset_recover_steps = 8
-
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Track previous position
-        self.Player.server.connect()
-        # sleep(2.0)  # Longer wait for connection to establish completely
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-        self.start_time = time.time()
-
-    def _reconnect_server(self):
-        try:
-            self.Player.server.shutdown()
-        except Exception:
-            pass
-
-        self.Player.server.connect()
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-
-    def _safe_receive_world_update(self, retries=1):
-        last_exc = None
-        for attempt in range(retries + 1):
-            try:
-                self.Player.server.receive()
-                self.Player.world.update()
-                return
-            except (ConnectionResetError, OSError) as exc:
-                last_exc = exc
-                if attempt >= retries:
-                    raise
-                self._reconnect_server()
-        if last_exc is not None:
-            raise last_exc
-
-    def debug_log(self, message):
-        print(message)
-        try:
-            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
-            with open(log_path, "a", encoding="utf-8") as f:
-                f.write(message + "\n")
-        except OSError:
-            pass
-
-    def observe(self, init=False):
-
-        """获取当前观测值"""
-        robot = self.Player.robot
-        world = self.Player.world
-
-        # Safety check: ensure data is available
-
-        # 计算目标速度
-        raw_target = self.target_position - world.global_position[:2]
-        velocity = MathOps.rotate_2d_vec(
-            raw_target,
-            -robot.global_orientation_euler[2],
-            is_rad=False
-        )
-
-        # 计算相对方向
-        rel_orientation = MathOps.vector_angle(velocity) * 0.3
-        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
-
-        velocity = np.concatenate([velocity, np.array([rel_orientation])])
-        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
-        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
-
-        # 关节状态
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        qpos_qvel_previous_action = np.concatenate([
-            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
-            radian_joint_speeds / 110.0 * self.train_sim_flip,
-            self.previous_action / 10.0,
-        ])
-
-        # 角速度
-        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
-
-        # 投影的重力方向
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-
-        # 组合观测
-        observation = np.concatenate([
-            qpos_qvel_previous_action,
-            ang_vel,
-            velocity,
-            projected_gravity,
-        ])
-
-        observation = np.clip(observation, -10.0, 10.0)
-        return observation.astype(np.float32)
-
-    def sync(self):
-        ''' Run a single simulation step '''
-        self._safe_receive_world_update(retries=1)
-        self.Player.robot.commit_motor_targets_pd()
-        self.Player.server.send()
-        if self._target_dt > 0.0:
-            now = time.time()
-            if self._last_sync_time is None:
-                self._last_sync_time = now
-                return
-            elapsed = now - self._last_sync_time
-            remaining = self._target_dt - elapsed
-            if remaining > 0.0:
-                time.sleep(remaining)
-                now = time.time()
-            self._last_sync_time = now
-
-    def debug_joint_status(self):
-        robot = self.Player.robot
-        actual_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        target_joint_positions = getattr(
-            self,
-            'target_joint_positions',
-            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
-        )
-        joint_error = actual_joint_positions - target_joint_positions
-        leg_slice = slice(11, None)
-
-        self.debug_log(
-            "[WalkDebug] "
-            f"step={self.step_counter} "
-            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
-            f"target_xy={np.round(self.target_position, 3).tolist()} "
-            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
-            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
-            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
-            f"fallen={self.Player.world.global_position[2] < 0.3}"
-        )
-        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
-
-    def reset(self, seed=None, options=None):
-        '''
-        Reset and stabilize the robot
-        Note: for some behaviors it would be better to reduce stabilization or add noise
-        '''
-        r = self.Player.robot
-        super().reset(seed=seed)
-        if seed is not None:
-            np.random.seed(seed)
-
-        length1 = 2  # randomize target distance
-        length2 = np.random.uniform(0.6, 1)  # randomize target distance
-        length3 = np.random.uniform(0.6, 1)  # randomize target distance
-        angle2 = np.random.uniform(-30, 30)  # randomize initial orientation
-        angle3 = np.random.uniform(-30, 30)  # randomize target direction
-
-        self.step_counter = 0
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
-        self.walk_cycle_step = 0
-
-        # 随机 beam 目标位置和朝向，增加训练多样性
-        beam_x = (random() - 0.5) * 10
-        beam_y = (random() - 0.5) * 10
-        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
-
-        for _ in range(5):
-            self._safe_receive_world_update(retries=2)
-            self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
-            self.Player.server.send()
-
-        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
-        finished_count = 0
-        for _ in range(50):
-            finished = self.Player.skills_manager.execute("Neutral")
-            self.sync()
-            if finished:
-                finished_count += 1
-                if finished_count >= 20:  # 假设需要连续20次完成才算成功
-                    break
-
-        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-            # Perturb waist + lower body only (10:), keep head/arms stable.
-            perturb_action[10:] = np.random.uniform(
-                -self.reset_joint_noise_rad,
-                self.reset_joint_noise_rad,
-                size=(self.no_of_actions - 10,)
-            )
-
-            for _ in range(self.reset_perturb_steps):
-                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-            for i in range(self.reset_recover_steps):
-                # Linearly fade perturbation to help policy start from near-neutral.
-                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-        # memory variables
-        self.sync()  
-        self.initial_position = np.array(self.Player.world.global_position[:2])
-        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions, np.float32)
-        # Build target in the robot's current forward direction instead of fixed global +x.
-        heading_deg = float(r.global_orientation_euler[2])
-        forward_offset = MathOps.rotate_2d_vec(np.array([length1, 0.0]), heading_deg, is_rad=False)
-        point1 = self.initial_position + forward_offset
-        point2 = point1 + MathOps.rotate_2d_vec(np.array([length2, 0]), angle2, is_rad=False)
-        point3 = point2 + MathOps.rotate_2d_vec(np.array([length3, 0]), angle3, is_rad=False)
-        self.point_list = [point1]
-        self.target_position = self.point_list[self.waypoint_index]
-        self.initial_height = self.Player.world.global_position[2]
-
-        return self.observe(True), {}
-
-    def render(self, mode='human', close=False):
-        return
-
-    def compute_reward(self, previous_pos, current_pos, action):
-        height = float(self.Player.world.global_position[2])
-        robot = self.Player.robot
-
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-        tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        ang_vel = np.deg2rad(robot.gyroscope)
-        ang_vel_mag = float(np.linalg.norm(ang_vel))
-
-        is_fallen = height < 0.55
-        if is_fallen:
-            # remain = max(0, 800 - self.step_counter)
-            # return -8.0 - 0.01 * remain
-            return -1.0
-        
-
-
-        # # 目标方向
-        # to_target = self.target_position - current_pos
-        # dist_to_target = float(np.linalg.norm(to_target))
-        # if dist_to_target < 0.5:
-        #     return 15.0
-
-        # forward_dir = to_target / dist_to_target if dist_to_target > 0.1 else np.array([1.0, 0.0])
-        # delta_pos = current_pos - previous_pos
-        # forward_step = float(np.dot(delta_pos, forward_dir))
-        # lateral_step = float(np.linalg.norm(delta_pos - forward_dir * forward_step))
-
-        # 奖励项
-        # progress_reward = 2 * forward_step
-        # lateral_penalty = -0.1 * lateral_step
-        alive_bonus = 2.0
-
-        # action_penalty = -0.01 * float(np.linalg.norm(action))
-        smoothness_penalty = -0.01 * float(np.linalg.norm(action - self.last_action_for_reward))
-
-        posture_penalty = -0.3 * (tilt_mag)
-        ang_vel_penalty = -0.02 * ang_vel_mag
-
-        # Use simulator joint readings in training frame to shape lateral stance.
-        joint_pos = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        ) * self.train_sim_flip
-        left_hip_roll = float(joint_pos[12])
-        right_hip_roll = float(joint_pos[18])
-        left_ankle_roll = float(joint_pos[16])
-        right_ankle_roll = float(joint_pos[22])
-
-        hip_spread = left_hip_roll - right_hip_roll
-        ankle_spread = left_ankle_roll - right_ankle_roll
-        stance_metric = 0.6 * abs(hip_spread) + 0.4 * abs(ankle_spread)
-
-        # Penalize narrow stance (feet too close) and scissoring (cross-leg pattern).
-        stance_collapse_penalty = -4.0 * max(0.0, self.min_stance_rad - stance_metric)
-        cross_leg_penalty = -1.2 * max(0.0, -(hip_spread * ankle_spread))
-
-        target_height = self.initial_height
-        height_error =  height - target_height
-        height_penalty = -0.5 * abs(height_error)  # 惩罚高度偏离，系数可调
-
-        # # 在 compute_reward 开头附近，添加高度变化率计算
-        # if not hasattr(self, 'last_height'):
-        #     self.last_height = height
-        #     self.last_height_time = self.step_counter  # 可选，用于时间间隔
-        # height_rate = height - self.last_height  # 正为上升，负为下降
-        # self.last_height = height
-
-        # 惩罚高度下降（负变化率）
-        # height_down_penalty = -5.0 * max(0, -height_rate)  # 系数可调，-height_rate 为正表示下降幅度
-
-        # # 在 compute_reward 中
-        # if self.step_counter > 50:
-        #     avg_prev_action = np.mean(self.prev_action_history, axis=0)
-        #     novelty = float(np.linalg.norm(action - avg_prev_action))
-        #     exploration_bonus = 0.05 * novelty
-        # else:
-        #     exploration_bonus = 0
-
-        # self.prev_action_history[self.history_idx] = action
-        # self.history_idx = (self.history_idx + 1) % 50
-
-
-        total = (
-            # progress_reward  + 
-                 alive_bonus + 
-                 # lateral_penalty +
-                 # action_penalty +
-                smoothness_penalty +
-                 posture_penalty
-                 + ang_vel_penalty
-                 + height_penalty
-                 + stance_collapse_penalty
-                 + cross_leg_penalty
-                #  + exploration_bonus
-                # + height_down_penalty
-                 )
-        if time.time() - self.start_time >= 600:
-            self.start_time = time.time()
-            print(
-                #   f"progress_reward:{progress_reward:.4f}",
-                #   f"lateral_penalty:{lateral_penalty:.4f}",
-                #   f"action_penalty:{action_penalty:.4f}"s, 
-                  f"height_penalty:{height_penalty:.4f}",
-                  f"smoothness_penalty:{smoothness_penalty:.4f},",
-                  f"posture_penalty:{posture_penalty:.4f}",
-                  f"stance_collapse_penalty:{stance_collapse_penalty:.4f}",
-                  f"cross_leg_penalty:{cross_leg_penalty:.4f}",
-                #   f"ang_vel_penalty:{ang_vel_penalty:.4f}",
-                #   f"height_down_penalty:{height_down_penalty:.4f}",
-                #   f"exploration_bonus:{exploration_bonus:.4f}"
-            )
-
-        return total
-
-
-
-    def step(self, action):
-
-        r = self.Player.robot
-        self.previous_action = action
-
-        self.target_joint_positions = (
-                # self.joint_nominal_position +
-                 self.scaling_factor * action
-        )
-        self.target_joint_positions *= self.train_sim_flip
-
-        for idx, target in enumerate(self.target_joint_positions):
-            r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=40, kd=1.0
-            )
-
-        self.previous_action = action
-
-        self.sync()  # run simulation step
-        self.step_counter += 1
-
-        if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
-            self.debug_joint_status()
-
-        current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
-
-        # Compute reward based on movement from previous step
-        reward = self.compute_reward(self.previous_pos, current_pos, action)
-
-        # Update previous position
-        self.previous_pos = current_pos.copy()
-        self.last_action_for_reward = action.copy()
-
-        # Fall detection and penalty
-        is_fallen = self.Player.world.global_position[2] < 0.55
-
-        # terminal state: the robot is falling or timeout
-        terminated = is_fallen or self.step_counter > 800 or self.route_completed
-        truncated = False
-
-        return self.observe(), reward, terminated, truncated, {}
-
-
-class Train(Train_Base):
-    def __init__(self, script) -> None:
-        super().__init__(script)
-
-    def train(self, args):
-
-        # --------------------------------------- Learning parameters
-        n_envs = int(os.environ.get("GYM_CPU_N_ENVS", "20"))
-        if n_envs < 1:
-            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
-        server_warmup_sec = float(os.environ.get("GYM_CPU_SERVER_WARMUP_SEC", "3.0"))
-        n_steps_per_env = int(os.environ.get("GYM_CPU_TRAIN_STEPS_PER_ENV", "256"))  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = int(os.environ.get("GYM_CPU_TRAIN_BATCH_SIZE", "512"))  # should be a factor of (n_steps_per_env * n_envs)
-        total_steps = 30000000
-        learning_rate = float(os.environ.get("GYM_CPU_TRAIN_LR", "3e-4"))
-        folder_name = f'Walk_R{self.robot_type}'
-        model_path = f'./scripts/gyms/logs/{folder_name}/'
-
-        print(f"Model path: {model_path}")
-        print(f"Using {n_envs} parallel environments")
-
-        # --------------------------------------- Run algorithm
-        def init_env(i_env, monitor=False):
-            def thunk():
-                env = WalkEnv(self.ip, self.server_p + i_env)
-                if monitor:
-                    env = Monitor(env)
-                return env
-
-            return thunk
-
-        server_log_dir = os.path.join(model_path, "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1, no_render=True, no_realtime=True)  # include 1 extra server for testing
-
-        # Wait for servers to start
-        print(f"Starting {n_envs + 1} rcssservermj servers...")
-        if server_warmup_sec > 0:
-            print(f"Waiting {server_warmup_sec:.1f}s for server warmup...")
-            sleep(server_warmup_sec)
-        print("Servers started, creating environments...")
-
-        env = SubprocVecEnv([init_env(i, monitor=True) for i in range(n_envs)])
-        # Use single-process eval env to avoid extra subprocess fragility during callback evaluation.
-        eval_env = DummyVecEnv([init_env(n_envs, monitor=True)])
-
-        try:
-            # Custom policy network architecture
-            policy_kwargs = dict(
-                net_arch=dict(
-                    pi=[512, 256, 128],  # Policy network: 3 layers
-                    vf=[512, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
-            )
-
-            if "model_file" in args:  # retrain
-                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
-                                 batch_size=minibatch_size, learning_rate=learning_rate)
-            else:  # train new model
-                model = PPO(
-                    "MlpPolicy",
-                    env=env,
-                    verbose=1,
-                    n_steps=n_steps_per_env,
-                    batch_size=minibatch_size,
-                    learning_rate=learning_rate,
-                    device="cpu",
-                    policy_kwargs=policy_kwargs,
-                    ent_coef=float(os.environ.get("GYM_CPU_TRAIN_ENT_COEF", "0.05")),  # Entropy coefficient for exploration
-                    clip_range=float(os.environ.get("GYM_CPU_TRAIN_CLIP_RANGE", "0.2")),  # PPO clipping parameter
-                    gae_lambda=0.95,  # GAE lambda
-                    gamma=float(os.environ.get("GYM_CPU_TRAIN_GAMMA", "0.95")), # Discount factor
-                    # target_kl=0.03,
-                    n_epochs=int(os.environ.get("GYM_CPU_TRAIN_EPOCHS", "5")),
-                    tensorboard_log=f"./scripts/gyms/logs/{folder_name}/tensorboard/"
-                )
-
-            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
-                                          eval_freq=n_steps_per_env * 20, save_freq=n_steps_per_env * 20, eval_eps=100,
-                                          backup_env_file=__file__)
-        except KeyboardInterrupt:
-            sleep(1)  # wait for child processes
-            print("\nctrl+c pressed, aborting...\n")
-            servers.kill()
-            return
-
-        env.close()
-        eval_env.close()
-        servers.kill()
-
-    def test(self, args):
-
-        # Uses different server and monitor ports
-        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        test_no_render = os.environ.get("GYM_CPU_TEST_NO_RENDER", "0") == "1"
-        test_no_realtime = os.environ.get("GYM_CPU_TEST_NO_REALTIME", "0") == "1"
-
-        server = Train_Server(
-            self.server_p - 1,
-            self.monitor_p,
-            1,
-            no_render=test_no_render,
-            no_realtime=test_no_realtime,
-        )
-        env = WalkEnv(self.ip, self.server_p - 1)
-        model = PPO.load(args["model_file"], env=env)
-
-        try:
-            self.export_model(args["model_file"], args["model_file"] + ".pkl",
-                              False)  # Export to pkl to create custom behavior
-            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
-        except KeyboardInterrupt:
-            print()
-
-        env.close()
-        server.kill()
-
-
-if __name__ == "__main__":
-    from types import SimpleNamespace
-
-    # 创建默认参数
-    script_args = SimpleNamespace(
-        args=SimpleNamespace(
-            i='127.0.0.1',  # Server IP
-            p=3100,  # Server port
-            m=3200,  # Monitor port
-            r=0,  # Robot type
-            t='Gym',  # Team name
-            u=1  # Uniform number
-        )
-    )
-
-    trainer = Train(script_args)
-
-    run_mode = os.environ.get("GYM_CPU_MODE", "train").strip().lower()
-
-    if run_mode == "test":
-        test_model_file = os.environ.get("GYM_CPU_TEST_MODEL", "scripts/gyms/logs/Walk_R0_004/best_model.zip")
-        test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Walk_R0_004/")
-        trainer.test({"model_file": test_model_file, "folder_dir": test_folder})
-    else:
-        retrain_model = os.environ.get("GYM_CPU_TRAIN_MODEL", "").strip()
-        if retrain_model:
-            trainer.train({"model_file": retrain_model})
-        else:
-            trainer.train({})

scripts/gyms/logs/stand_stable_0.1/Walk.py

@@ -1,626 +0,0 @@
-import os
-import numpy as np
-import math
-import time
-from time import sleep
-from random import random
-from random import uniform
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.vec_env import SubprocVecEnv
-
-import gymnasium as gym
-from gymnasium import spaces
-
-from scripts.commons.Train_Base import Train_Base
-from scripts.commons.Server import Server as Train_Server
-
-from agent.base_agent import Base_Agent
-from utils.math_ops import MathOps
-
-from scipy.spatial.transform import Rotation as R
-
-'''
-Objective:
-Learn how to run forward using step primitive
-----------
-- class Basic_Run: implements an OpenAI custom gym
-- class Train:  implements algorithms to train a new model or test an existing model
-'''
-
-
-class WalkEnv(gym.Env):
-    def __init__(self, ip, server_p) -> None:
-
-        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
-        self.Player = player = Base_Agent(
-            team_name="Gym",
-            number=1,
-            host=ip,
-            port=server_p
-        )
-        self.robot_type = self.Player.robot
-        self.step_counter = 0  # to limit episode size
-        self.force_play_on = True
-
-        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
-        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
-        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
-        self.isfallen = False
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.debug_every_n_steps = 5
-        self.enable_debug_joint_status = False
-        self.calibrate_nominal_from_neutral = True
-        self.auto_calibrate_train_sim_flip = True
-        self.nominal_calibrated_once = False
-        self.flip_calibrated_once = False
-        self._target_hz = 0.0
-        self._target_dt = 0.0
-        self._last_sync_time = None
-        target_hz_env = 0
-        if target_hz_env:
-            try:
-                self._target_hz = float(target_hz_env)
-            except ValueError:
-                self._target_hz = 0.0
-        if self._target_hz > 0.0:
-            self._target_dt = 1.0 / self._target_hz
-
-        # State space
-        # 原始观测大小: 78
-        obs_size = 78
-        self.obs = np.zeros(obs_size, np.float32)
-        self.observation_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(obs_size,),
-            dtype=np.float32
-        )
-
-        action_dim = len(self.Player.robot.ROBOT_MOTORS)
-        self.no_of_actions = action_dim
-        self.action_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(action_dim,),
-            dtype=np.float32
-        )
-
-        # 中立姿态
-        self.joint_nominal_position = np.array(
-            [
-                0.0,
-                0.0,
-                0.0,
-                1.4,
-                0.0,
-                -0.4,
-                0.0,
-                -1.4,
-                0.0,
-                0.4,
-                0.0,
-                -0.4,
-                0.0,
-                0.0,
-                0.8,
-                -0.4,
-                0.0,
-                0.4,
-                0.0,
-                0.0,
-                -0.8,
-                0.4,
-                0.0,
-            ]
-        )
-        self.joint_nominal_position = np.zeros(self.no_of_actions)
-        self.train_sim_flip = np.array(
-            [
-                1.0,  # 0: Head_yaw (he1)
-                -1.0,  # 1: Head_pitch (he2)
-                1.0,  # 2: Left_Shoulder_Pitch (lae1)
-                -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                -1.0,  # 4: Left_Elbow_Pitch (lae3)
-                1.0,  # 5: Left_Elbow_Yaw (lae4)
-                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                -1.0,  # 7: Right_Shoulder_Roll (rae2)
-                1.0,  # 8: Right_Elbow_Pitch (rae3)
-                1.0,  # 9: Right_Elbow_Yaw (rae4)
-                1.0,  # 10: Waist (te1)
-                1.0,  # 11: Left_Hip_Pitch (lle1)
-                -1.0,  # 12: Left_Hip_Roll (lle2)
-                -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,  # 14: Left_Knee_Pitch (lle4)
-                1.0,  # 15: Left_Ankle_Pitch (lle5)
-                -1.0,  # 16: Left_Ankle_Roll (lle6)
-                -1.0,  # 17: Right_Hip_Pitch (rle1)
-                -1.0,  # 18: Right_Hip_Roll (rle2)
-                -1.0,  # 19: Right_Hip_Yaw (rle3)
-                -1.0,  # 20: Right_Knee_Pitch (rle4)
-                -1.0,  # 21: Right_Ankle_Pitch (rle5)
-                -1.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-
-        self.scaling_factor = 0.3
-        # self.scaling_factor = 1
-
-        # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = True
-        self.reset_beam_yaw_range_deg = 180  # randomize target direction fully to encourage learning a real walk instead of a fixed gait
-        self.reset_joint_noise_rad = 0.015
-        self.reset_perturb_steps = 3
-        self.reset_recover_steps = 8
-
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Track previous position
-        self.Player.server.connect()
-        # sleep(2.0)  # Longer wait for connection to establish completely
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-        self.start_time = time.time()
-
-    def debug_log(self, message):
-        print(message)
-        try:
-            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
-            with open(log_path, "a", encoding="utf-8") as f:
-                f.write(message + "\n")
-        except OSError:
-            pass
-
-    def observe(self, init=False):
-
-        """获取当前观测值"""
-        robot = self.Player.robot
-        world = self.Player.world
-
-        # Safety check: ensure data is available
-
-        # 计算目标速度
-        raw_target = self.target_position - world.global_position[:2]
-        velocity = MathOps.rotate_2d_vec(
-            raw_target,
-            -robot.global_orientation_euler[2],
-            is_rad=False
-        )
-
-        # 计算相对方向
-        rel_orientation = MathOps.vector_angle(velocity) * 0.3
-        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
-
-        velocity = np.concatenate([velocity, np.array([rel_orientation])])
-        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
-        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
-
-        # 关节状态
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        qpos_qvel_previous_action = np.concatenate([
-            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
-            radian_joint_speeds / 110.0 * self.train_sim_flip,
-            self.previous_action / 10.0,
-        ])
-
-        # 角速度
-        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
-
-        # 投影的重力方向
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-
-        # 组合观测
-        observation = np.concatenate([
-            qpos_qvel_previous_action,
-            ang_vel,
-            velocity,
-            projected_gravity,
-        ])
-
-        observation = np.clip(observation, -10.0, 10.0)
-        return observation.astype(np.float32)
-
-    def sync(self):
-        ''' Run a single simulation step '''
-        self.Player.server.receive()
-        self.Player.world.update()
-        self.Player.robot.commit_motor_targets_pd()
-        self.Player.server.send()
-        if self._target_dt > 0.0:
-            now = time.time()
-            if self._last_sync_time is None:
-                self._last_sync_time = now
-                return
-            elapsed = now - self._last_sync_time
-            remaining = self._target_dt - elapsed
-            if remaining > 0.0:
-                time.sleep(remaining)
-                now = time.time()
-            self._last_sync_time = now
-
-    def debug_joint_status(self):
-        robot = self.Player.robot
-        actual_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        target_joint_positions = getattr(
-            self,
-            'target_joint_positions',
-            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
-        )
-        joint_error = actual_joint_positions - target_joint_positions
-        leg_slice = slice(11, None)
-
-        self.debug_log(
-            "[WalkDebug] "
-            f"step={self.step_counter} "
-            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
-            f"target_xy={np.round(self.target_position, 3).tolist()} "
-            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
-            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
-            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
-            f"fallen={self.Player.world.global_position[2] < 0.3}"
-        )
-        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
-
-    def reset(self, seed=None, options=None):
-        '''
-        Reset and stabilize the robot
-        Note: for some behaviors it would be better to reduce stabilization or add noise
-        '''
-        r = self.Player.robot
-        super().reset(seed=seed)
-        if seed is not None:
-            np.random.seed(seed)
-
-        length1 = 2  # randomize target distance
-        length2 = np.random.uniform(0.6, 1)  # randomize target distance
-        length3 = np.random.uniform(0.6, 1)  # randomize target distance
-        angle2 = np.random.uniform(-30, 30)  # randomize initial orientation
-        angle3 = np.random.uniform(-30, 30)  # randomize target direction
-
-        self.step_counter = 0
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
-        self.walk_cycle_step = 0
-
-        # 随机 beam 目标位置和朝向，增加训练多样性
-        beam_x = (random() - 0.5) * 10
-        beam_y = (random() - 0.5) * 10
-        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
-
-        for _ in range(5):
-            self.Player.server.receive()
-            self.Player.world.update()
-            self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
-            self.Player.server.send()
-
-        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
-        finished_count = 0
-        for _ in range(50):
-            finished = self.Player.skills_manager.execute("Neutral")
-            self.sync()
-            if finished:
-                finished_count += 1
-                if finished_count >= 20:  # 假设需要连续20次完成才算成功
-                    break
-
-        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-            # Perturb waist + lower body only (10:), keep head/arms stable.
-            perturb_action[10:] = np.random.uniform(
-                -self.reset_joint_noise_rad,
-                self.reset_joint_noise_rad,
-                size=(self.no_of_actions - 10,)
-            )
-
-            for _ in range(self.reset_perturb_steps):
-                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-            for i in range(self.reset_recover_steps):
-                # Linearly fade perturbation to help policy start from near-neutral.
-                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-        # memory variables
-        self.sync()  
-        self.initial_position = np.array(self.Player.world.global_position[:2])
-        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions, np.float32)
-        # Build target in the robot's current forward direction instead of fixed global +x.
-        heading_deg = float(r.global_orientation_euler[2])
-        forward_offset = MathOps.rotate_2d_vec(np.array([length1, 0.0]), heading_deg, is_rad=False)
-        point1 = self.initial_position + forward_offset
-        point2 = point1 + MathOps.rotate_2d_vec(np.array([length2, 0]), angle2, is_rad=False)
-        point3 = point2 + MathOps.rotate_2d_vec(np.array([length3, 0]), angle3, is_rad=False)
-        self.point_list = [point1]
-        self.target_position = self.point_list[self.waypoint_index]
-        self.initial_height = self.Player.world.global_position[2]
-
-        return self.observe(True), {}
-
-    def render(self, mode='human', close=False):
-        return
-
-    def compute_reward(self, previous_pos, current_pos, action):
-        height = float(self.Player.world.global_position[2])
-
-        orientation_quat_inv = R.from_quat(self.Player.robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-        tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        ang_vel = np.deg2rad(self.Player.robot.gyroscope)
-        ang_vel_mag = float(np.linalg.norm(ang_vel))
-
-        is_fallen = height < 0.3
-        if is_fallen:
-            # remain = max(0, 800 - self.step_counter)
-            # return -8.0 - 0.01 * remain
-            return -1.0
-        
-
-
-        # # 目标方向
-        # to_target = self.target_position - current_pos
-        # dist_to_target = float(np.linalg.norm(to_target))
-        # if dist_to_target < 0.5:
-        #     return 15.0
-
-        # forward_dir = to_target / dist_to_target if dist_to_target > 0.1 else np.array([1.0, 0.0])
-        # delta_pos = current_pos - previous_pos
-        # forward_step = float(np.dot(delta_pos, forward_dir))
-        # lateral_step = float(np.linalg.norm(delta_pos - forward_dir * forward_step))
-
-        # 奖励项
-        # progress_reward = 2 * forward_step
-        # lateral_penalty = -0.1 * lateral_step
-        alive_bonus = 2.0
-
-        # action_penalty = -0.01 * float(np.linalg.norm(action))
-        smoothness_penalty = -0.01 * float(np.linalg.norm(action - self.last_action_for_reward))
-
-        posture_penalty = -0.3 * (tilt_mag)
-        ang_vel_penalty = -0.02 * ang_vel_mag
-
-        target_height = self.initial_height
-        height_error =  height - target_height
-        height_penalty = -0.5 * abs(height_error)  # 惩罚高度偏离，系数可调
-
-        # # 在 compute_reward 开头附近，添加高度变化率计算
-        # if not hasattr(self, 'last_height'):
-        #     self.last_height = height
-        #     self.last_height_time = self.step_counter  # 可选，用于时间间隔
-        # height_rate = height - self.last_height  # 正为上升，负为下降
-        # self.last_height = height
-
-        # 惩罚高度下降（负变化率）
-        # height_down_penalty = -5.0 * max(0, -height_rate)  # 系数可调，-height_rate 为正表示下降幅度
-
-        # # 在 compute_reward 中
-        # if self.step_counter > 50:
-        #     avg_prev_action = np.mean(self.prev_action_history, axis=0)
-        #     novelty = float(np.linalg.norm(action - avg_prev_action))
-        #     exploration_bonus = 0.05 * novelty
-        # else:
-        #     exploration_bonus = 0
-
-        # self.prev_action_history[self.history_idx] = action
-        # self.history_idx = (self.history_idx + 1) % 50
-
-
-        total = (
-            # progress_reward  + 
-                 alive_bonus + 
-                 # lateral_penalty +
-                 # action_penalty +
-                smoothness_penalty +
-                 posture_penalty
-                 + ang_vel_penalty
-                 + height_penalty
-                #  + exploration_bonus
-                # + height_down_penalty
-                 )
-        if time.time() - self.start_time >= 1200:
-            self.start_time = time.time()
-            print(
-                #   f"progress_reward:{progress_reward:.4f}",
-                #   f"lateral_penalty:{lateral_penalty:.4f}",
-                #   f"action_penalty:{action_penalty:.4f}"s, 
-                  f"height_penalty:{height_penalty:.4f}",
-                  f"smoothness_penalty:{smoothness_penalty:.4f},",
-                  f"posture_penalty:{posture_penalty:.4f}",
-                #   f"ang_vel_penalty:{ang_vel_penalty:.4f}",
-                #   f"height_down_penalty:{height_down_penalty:.4f}",
-                #   f"exploration_bonus:{exploration_bonus:.4f}"
-            )
-
-        return total
-
-
-
-    def step(self, action):
-
-        r = self.Player.robot
-        self.previous_action = action
-
-        self.target_joint_positions = (
-                # self.joint_nominal_position +
-                 self.scaling_factor * action
-        )
-        self.target_joint_positions *= self.train_sim_flip
-
-        for idx, target in enumerate(self.target_joint_positions):
-            r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=40, kd=1.0
-            )
-
-        self.previous_action = action
-
-        self.sync()  # run simulation step
-        self.step_counter += 1
-
-        if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
-            self.debug_joint_status()
-
-        current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
-
-        # Compute reward based on movement from previous step
-        reward = self.compute_reward(self.previous_pos, current_pos, action)
-
-        # Update previous position
-        self.previous_pos = current_pos.copy()
-        self.last_action_for_reward = action.copy()
-
-        # Fall detection and penalty
-        is_fallen = self.Player.world.global_position[2] < 0.3
-
-        # terminal state: the robot is falling or timeout
-        terminated = is_fallen or self.step_counter > 800 or self.route_completed
-        truncated = False
-
-        return self.observe(), reward, terminated, truncated, {}
-
-
-class Train(Train_Base):
-    def __init__(self, script) -> None:
-        super().__init__(script)
-
-    def train(self, args):
-
-        # --------------------------------------- Learning parameters
-        n_envs = 20  # Reduced from 8 to decrease CPU/network pressure during init
-        if n_envs < 1:
-            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
-        n_steps_per_env = 256  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = 512  # should be a factor of (n_steps_per_env * n_envs)
-        total_steps = 30000000
-        learning_rate = 3e-4
-        folder_name = f'Walk_R{self.robot_type}'
-        model_path = f'./scripts/gyms/logs/{folder_name}/'
-
-        print(f"Model path: {model_path}")
-        print(f"Using {n_envs} parallel environments")
-
-        # --------------------------------------- Run algorithm
-        def init_env(i_env):
-            def thunk():
-                return WalkEnv(self.ip, self.server_p + i_env)
-
-            return thunk
-
-        server_log_dir = os.path.join(model_path, "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1)  # include 1 extra server for testing
-
-        # Wait for servers to start
-        print(f"Starting {n_envs + 1} rcssservermj servers...")
-        print("Servers started, creating environments...")
-
-        env = SubprocVecEnv([init_env(i) for i in range(n_envs)])
-        eval_env = SubprocVecEnv([init_env(n_envs)])
-
-        try:
-            # Custom policy network architecture
-            policy_kwargs = dict(
-                net_arch=dict(
-                    pi=[512, 256, 128],  # Policy network: 3 layers
-                    vf=[512, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
-            )
-
-            if "model_file" in args:  # retrain
-                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
-                                 batch_size=minibatch_size, learning_rate=learning_rate)
-            else:  # train new model
-                model = PPO(
-                    "MlpPolicy",
-                    env=env,
-                    verbose=1,
-                    n_steps=n_steps_per_env,
-                    batch_size=minibatch_size,
-                    learning_rate=learning_rate,
-                    device="cpu",
-                    policy_kwargs=policy_kwargs,
-                    ent_coef=0.05,  # Entropy coefficient for exploration
-                    # clip_range=0.13,  # PPO clipping parameter
-                    gae_lambda=0.95,  # GAE lambda
-                    gamma=0.95 , # Discount factor
-                    target_kl=0.03,
-                    n_epochs=5
-                )
-
-            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
-                                          eval_freq=n_steps_per_env * 10, save_freq=n_steps_per_env * 10,
-                                          backup_env_file=__file__)
-        except KeyboardInterrupt:
-            sleep(1)  # wait for child processes
-            print("\nctrl+c pressed, aborting...\n")
-            servers.kill()
-            return
-
-        env.close()
-        eval_env.close()
-        servers.kill()
-
-    def test(self, args):
-
-        # Uses different server and monitor ports
-        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        server = Train_Server(self.server_p - 1, self.monitor_p, 1)
-        env = WalkEnv(self.ip, self.server_p - 1)
-        model = PPO.load(args["model_file"], env=env)
-
-        try:
-            self.export_model(args["model_file"], args["model_file"] + ".pkl",
-                              False)  # Export to pkl to create custom behavior
-            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
-        except KeyboardInterrupt:
-            print()
-
-        env.close()
-        server.kill()
-
-
-if __name__ == "__main__":
-    from types import SimpleNamespace
-
-    # 创建默认参数
-    script_args = SimpleNamespace(
-        args=SimpleNamespace(
-            i='127.0.0.1',  # Server IP
-            p=3100,  # Server port
-            m=3200,  # Monitor port
-            r=0,  # Robot type
-            t='Gym',  # Team name
-            u=1  # Uniform number
-        )
-    )
-
-    trainer = Train(script_args)
-    trainer.train({})
-    # trainer.test({"model_file": "scripts/gyms/logs/Walk_R0_000/best_model.zip",
-                #   "folder_dir": "scripts/gyms/logs/Walk_R0_000/",})

scripts/gyms/logs/stand_stable_final/Walk.py

@@ -1,705 +0,0 @@
-import os
-import numpy as np
-import math
-import time
-from time import sleep
-from random import random
-from random import uniform
-from itertools import count
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.monitor import Monitor
-from stable_baselines3.common.vec_env import SubprocVecEnv, DummyVecEnv
-
-import gymnasium as gym
-from gymnasium import spaces
-
-from scripts.commons.Train_Base import Train_Base
-from scripts.commons.Server import Server as Train_Server
-
-from agent.base_agent import Base_Agent
-from utils.math_ops import MathOps
-
-from scipy.spatial.transform import Rotation as R
-
-'''
-Objective:
-Learn how to run forward using step primitive
-----------
-- class Basic_Run: implements an OpenAI custom gym
-- class Train:  implements algorithms to train a new model or test an existing model
-'''
-
-
-class WalkEnv(gym.Env):
-    def __init__(self, ip, server_p) -> None:
-
-        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
-        self.Player = player = Base_Agent(
-            team_name="Gym",
-            number=1,
-            host=ip,
-            port=server_p
-        )
-        self.robot_type = self.Player.robot
-        self.step_counter = 0  # to limit episode size
-        self.force_play_on = True
-
-        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
-        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
-        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
-        self.isfallen = False
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.debug_every_n_steps = 5
-        self.enable_debug_joint_status = False
-        self.calibrate_nominal_from_neutral = True
-        self.auto_calibrate_train_sim_flip = True
-        self.nominal_calibrated_once = False
-        self.flip_calibrated_once = False
-        self._target_hz = 0.0
-        self._target_dt = 0.0
-        self._last_sync_time = None
-        target_hz_env = 0
-        if target_hz_env:
-            try:
-                self._target_hz = float(target_hz_env)
-            except ValueError:
-                self._target_hz = 0.0
-        if self._target_hz > 0.0:
-            self._target_dt = 1.0 / self._target_hz
-
-        # State space
-        # 原始观测大小: 78
-        obs_size = 78
-        self.obs = np.zeros(obs_size, np.float32)
-        self.observation_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(obs_size,),
-            dtype=np.float32
-        )
-
-        action_dim = len(self.Player.robot.ROBOT_MOTORS)
-        self.no_of_actions = action_dim
-        self.action_space = spaces.Box(
-            low=-10.0,
-            high=10.0,
-            shape=(action_dim,),
-            dtype=np.float32
-        )
-
-        # 中立姿态
-        self.joint_nominal_position = np.array(
-            [
-                0.0,
-                0.0,
-                0.0,
-                1.4,
-                0.0,
-                -0.4,
-                0.0,
-                -1.4,
-                0.0,
-                0.4,
-                0.0,
-                -0.4,
-                0.0,
-                0.0,
-                0.8,
-                -0.4,
-                0.0,
-                0.4,
-                0.0,
-                0.0,
-                -0.8,
-                0.4,
-                0.0,
-            ]
-        )
-        self.joint_nominal_position = np.zeros(self.no_of_actions)
-        self.train_sim_flip = np.array(
-            [
-                1.0,  # 0: Head_yaw (he1)
-                -1.0,  # 1: Head_pitch (he2)
-                1.0,  # 2: Left_Shoulder_Pitch (lae1)
-                -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                -1.0,  # 4: Left_Elbow_Pitch (lae3)
-                1.0,  # 5: Left_Elbow_Yaw (lae4)
-                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                -1.0,  # 7: Right_Shoulder_Roll (rae2)
-                1.0,  # 8: Right_Elbow_Pitch (rae3)
-                1.0,  # 9: Right_Elbow_Yaw (rae4)
-                1.0,  # 10: Waist (te1)
-                1.0,  # 11: Left_Hip_Pitch (lle1)
-                -1.0,  # 12: Left_Hip_Roll (lle2)
-                -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,  # 14: Left_Knee_Pitch (lle4)
-                1.0,  # 15: Left_Ankle_Pitch (lle5)
-                -1.0,  # 16: Left_Ankle_Roll (lle6)
-                -1.0,  # 17: Right_Hip_Pitch (rle1)
-                -1.0,  # 18: Right_Hip_Roll (rle2)
-                -1.0,  # 19: Right_Hip_Yaw (rle3)
-                -1.0,  # 20: Right_Knee_Pitch (rle4)
-                -1.0,  # 21: Right_Ankle_Pitch (rle5)
-                -1.0,  # 22: Right_Ankle_Roll (rle6)
-            ]
-        )
-
-        self.scaling_factor = 0.3
-        # self.scaling_factor = 1
-
-        # Encourage a minimum lateral stance so the policy avoids feet overlap.
-        self.min_stance_rad = 0.10
-
-        # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = False
-        self.reset_beam_yaw_range_deg = 180  # randomize target direction fully to encourage learning a real walk instead of a fixed gait
-        self.reset_joint_noise_rad = 0.015
-        self.reset_perturb_steps = 3
-        self.reset_recover_steps = 8
-
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Track previous position
-        self.Player.server.connect()
-        # sleep(2.0)  # Longer wait for connection to establish completely
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-        self.start_time = time.time()
-
-    def _reconnect_server(self):
-        try:
-            self.Player.server.shutdown()
-        except Exception:
-            pass
-
-        self.Player.server.connect()
-        self.Player.server.send_immediate(
-            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
-        )
-
-    def _safe_receive_world_update(self, retries=1):
-        last_exc = None
-        for attempt in range(retries + 1):
-            try:
-                self.Player.server.receive()
-                self.Player.world.update()
-                return
-            except (ConnectionResetError, OSError) as exc:
-                last_exc = exc
-                if attempt >= retries:
-                    raise
-                self._reconnect_server()
-        if last_exc is not None:
-            raise last_exc
-
-    def debug_log(self, message):
-        print(message)
-        try:
-            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
-            with open(log_path, "a", encoding="utf-8") as f:
-                f.write(message + "\n")
-        except OSError:
-            pass
-
-    def observe(self, init=False):
-
-        """获取当前观测值"""
-        robot = self.Player.robot
-        world = self.Player.world
-
-        # Safety check: ensure data is available
-
-        # 计算目标速度
-        raw_target = self.target_position - world.global_position[:2]
-        velocity = MathOps.rotate_2d_vec(
-            raw_target,
-            -robot.global_orientation_euler[2],
-            is_rad=False
-        )
-
-        # 计算相对方向
-        rel_orientation = MathOps.vector_angle(velocity) * 0.3
-        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
-
-        velocity = np.concatenate([velocity, np.array([rel_orientation])])
-        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
-        velocity[1] = np.clip(velocity[1], -0.25, 0.25)
-
-        # 关节状态
-        radian_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
-
-        qpos_qvel_previous_action = np.concatenate([
-            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
-            radian_joint_speeds / 110.0 * self.train_sim_flip,
-            self.previous_action / 10.0,
-        ])
-
-        # 角速度
-        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
-
-        # 投影的重力方向
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-
-        # 组合观测
-        observation = np.concatenate([
-            qpos_qvel_previous_action,
-            ang_vel,
-            velocity,
-            projected_gravity,
-        ])
-
-        observation = np.clip(observation, -10.0, 10.0)
-        return observation.astype(np.float32)
-
-    def sync(self):
-        ''' Run a single simulation step '''
-        self._safe_receive_world_update(retries=1)
-        self.Player.robot.commit_motor_targets_pd()
-        self.Player.server.send()
-        if self._target_dt > 0.0:
-            now = time.time()
-            if self._last_sync_time is None:
-                self._last_sync_time = now
-                return
-            elapsed = now - self._last_sync_time
-            remaining = self._target_dt - elapsed
-            if remaining > 0.0:
-                time.sleep(remaining)
-                now = time.time()
-            self._last_sync_time = now
-
-    def debug_joint_status(self):
-        robot = self.Player.robot
-        actual_joint_positions = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        )
-        target_joint_positions = getattr(
-            self,
-            'target_joint_positions',
-            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
-        )
-        joint_error = actual_joint_positions - target_joint_positions
-        leg_slice = slice(11, None)
-
-        self.debug_log(
-            "[WalkDebug] "
-            f"step={self.step_counter} "
-            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
-            f"target_xy={np.round(self.target_position, 3).tolist()} "
-            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
-            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
-            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
-            f"fallen={self.Player.world.global_position[2] < 0.3}"
-        )
-        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
-
-    def reset(self, seed=None, options=None):
-        '''
-        Reset and stabilize the robot
-        Note: for some behaviors it would be better to reduce stabilization or add noise
-        '''
-        r = self.Player.robot
-        super().reset(seed=seed)
-        if seed is not None:
-            np.random.seed(seed)
-
-        length1 = 2  # randomize target distance
-        length2 = np.random.uniform(0.6, 1)  # randomize target distance
-        length3 = np.random.uniform(0.6, 1)  # randomize target distance
-        angle2 = np.random.uniform(-30, 30)  # randomize initial orientation
-        angle3 = np.random.uniform(-30, 30)  # randomize target direction
-
-        self.step_counter = 0
-        self.waypoint_index = 0
-        self.route_completed = False
-        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
-        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
-        self.walk_cycle_step = 0
-
-        # 随机 beam 目标位置和朝向，增加训练多样性
-        beam_x = (random() - 0.5) * 10
-        beam_y = (random() - 0.5) * 10
-        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
-
-        for _ in range(5):
-            self._safe_receive_world_update(retries=2)
-            self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
-            self.Player.server.send()
-
-        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
-        finished_count = 0
-        for _ in range(50):
-            finished = self.Player.skills_manager.execute("Neutral")
-            self.sync()
-            if finished:
-                finished_count += 1
-                if finished_count >= 20:  # 假设需要连续20次完成才算成功
-                    break
-
-        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-            # Perturb waist + lower body only (10:), keep head/arms stable.
-            perturb_action[10:] = np.random.uniform(
-                -self.reset_joint_noise_rad,
-                self.reset_joint_noise_rad,
-                size=(self.no_of_actions - 10,)
-            )
-
-            for _ in range(self.reset_perturb_steps):
-                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-            for i in range(self.reset_recover_steps):
-                # Linearly fade perturbation to help policy start from near-neutral.
-                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-                for idx, target in enumerate(target_joint_positions):
-                    r.set_motor_target_position(
-                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-                    )
-                self.sync()
-
-        # memory variables
-        self.sync()  
-        self.initial_position = np.array(self.Player.world.global_position[:2])
-        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions, np.float32)
-        # Build target in the robot's current forward direction instead of fixed global +x.
-        heading_deg = float(r.global_orientation_euler[2])
-        forward_offset = MathOps.rotate_2d_vec(np.array([length1, 0.0]), heading_deg, is_rad=False)
-        point1 = self.initial_position + forward_offset
-        point2 = point1 + MathOps.rotate_2d_vec(np.array([length2, 0]), angle2, is_rad=False)
-        point3 = point2 + MathOps.rotate_2d_vec(np.array([length3, 0]), angle3, is_rad=False)
-        self.point_list = [point1]
-        self.target_position = self.point_list[self.waypoint_index]
-        self.initial_height = self.Player.world.global_position[2]
-
-        return self.observe(True), {}
-
-    def render(self, mode='human', close=False):
-        return
-
-    def compute_reward(self, previous_pos, current_pos, action):
-        height = float(self.Player.world.global_position[2])
-        robot = self.Player.robot
-
-        orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
-        projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
-        tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        ang_vel = np.deg2rad(robot.gyroscope)
-        ang_vel_mag = float(np.linalg.norm(ang_vel))
-
-        is_fallen = height < 0.55
-        if is_fallen:
-            # remain = max(0, 800 - self.step_counter)
-            # return -8.0 - 0.01 * remain
-            return -1.0
-        
-
-
-        # # 目标方向
-        # to_target = self.target_position - current_pos
-        # dist_to_target = float(np.linalg.norm(to_target))
-        # if dist_to_target < 0.5:
-        #     return 15.0
-
-        # forward_dir = to_target / dist_to_target if dist_to_target > 0.1 else np.array([1.0, 0.0])
-        # delta_pos = current_pos - previous_pos
-        # forward_step = float(np.dot(delta_pos, forward_dir))
-        # lateral_step = float(np.linalg.norm(delta_pos - forward_dir * forward_step))
-
-        # 奖励项
-        # progress_reward = 2 * forward_step
-        # lateral_penalty = -0.1 * lateral_step
-        alive_bonus = 2.0
-
-        # action_penalty = -0.01 * float(np.linalg.norm(action))
-        smoothness_penalty = -0.01 * float(np.linalg.norm(action - self.last_action_for_reward))
-
-        posture_penalty = -0.3 * (tilt_mag)
-        ang_vel_penalty = -0.02 * ang_vel_mag
-
-        # Use simulator joint readings in training frame to shape lateral stance.
-        joint_pos = np.deg2rad(
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
-        ) * self.train_sim_flip
-        left_hip_roll = float(joint_pos[12])
-        right_hip_roll = float(joint_pos[18])
-        left_ankle_roll = float(joint_pos[16])
-        right_ankle_roll = float(joint_pos[22])
-
-        hip_spread = left_hip_roll - right_hip_roll
-        ankle_spread = left_ankle_roll - right_ankle_roll
-        stance_metric = 0.6 * abs(hip_spread) + 0.4 * abs(ankle_spread)
-
-        # Penalize narrow stance (feet too close) and scissoring (cross-leg pattern).
-        stance_collapse_penalty = -4.0 * max(0.0, self.min_stance_rad - stance_metric)
-        cross_leg_penalty = -1.2 * max(0.0, -(hip_spread * ankle_spread))
-
-        target_height = self.initial_height
-        height_error =  height - target_height
-        height_penalty = -0.5 * abs(height_error)  # 惩罚高度偏离，系数可调
-
-        # # 在 compute_reward 开头附近，添加高度变化率计算
-        # if not hasattr(self, 'last_height'):
-        #     self.last_height = height
-        #     self.last_height_time = self.step_counter  # 可选，用于时间间隔
-        # height_rate = height - self.last_height  # 正为上升，负为下降
-        # self.last_height = height
-
-        # 惩罚高度下降（负变化率）
-        # height_down_penalty = -5.0 * max(0, -height_rate)  # 系数可调，-height_rate 为正表示下降幅度
-
-        # # 在 compute_reward 中
-        # if self.step_counter > 50:
-        #     avg_prev_action = np.mean(self.prev_action_history, axis=0)
-        #     novelty = float(np.linalg.norm(action - avg_prev_action))
-        #     exploration_bonus = 0.05 * novelty
-        # else:
-        #     exploration_bonus = 0
-
-        # self.prev_action_history[self.history_idx] = action
-        # self.history_idx = (self.history_idx + 1) % 50
-
-
-        total = (
-            # progress_reward  + 
-                 alive_bonus + 
-                 # lateral_penalty +
-                 # action_penalty +
-                smoothness_penalty +
-                 posture_penalty
-                 + ang_vel_penalty
-                 + height_penalty
-                 + stance_collapse_penalty
-                 + cross_leg_penalty
-                #  + exploration_bonus
-                # + height_down_penalty
-                 )
-        if time.time() - self.start_time >= 600:
-            self.start_time = time.time()
-            print(
-                #   f"progress_reward:{progress_reward:.4f}",
-                #   f"lateral_penalty:{lateral_penalty:.4f}",
-                #   f"action_penalty:{action_penalty:.4f}"s, 
-                  f"height_penalty:{height_penalty:.4f}",
-                  f"smoothness_penalty:{smoothness_penalty:.4f},",
-                  f"posture_penalty:{posture_penalty:.4f}",
-                  f"stance_collapse_penalty:{stance_collapse_penalty:.4f}",
-                  f"cross_leg_penalty:{cross_leg_penalty:.4f}",
-                #   f"ang_vel_penalty:{ang_vel_penalty:.4f}",
-                #   f"height_down_penalty:{height_down_penalty:.4f}",
-                #   f"exploration_bonus:{exploration_bonus:.4f}"
-            )
-
-        return total
-
-
-
-    def step(self, action):
-
-        r = self.Player.robot
-        self.previous_action = action
-
-        self.target_joint_positions = (
-                # self.joint_nominal_position +
-                 self.scaling_factor * action
-        )
-        self.target_joint_positions *= self.train_sim_flip
-
-        for idx, target in enumerate(self.target_joint_positions):
-            r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=40, kd=1.0
-            )
-
-        self.previous_action = action
-
-        self.sync()  # run simulation step
-        self.step_counter += 1
-
-        if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
-            self.debug_joint_status()
-
-        current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
-
-        # Compute reward based on movement from previous step
-        reward = self.compute_reward(self.previous_pos, current_pos, action)
-
-        # Update previous position
-        self.previous_pos = current_pos.copy()
-        self.last_action_for_reward = action.copy()
-
-        # Fall detection and penalty
-        is_fallen = self.Player.world.global_position[2] < 0.55
-
-        # terminal state: the robot is falling or timeout
-        terminated = is_fallen or self.step_counter > 800 or self.route_completed
-        truncated = False
-
-        return self.observe(), reward, terminated, truncated, {}
-
-
-class Train(Train_Base):
-    def __init__(self, script) -> None:
-        super().__init__(script)
-
-    def train(self, args):
-
-        # --------------------------------------- Learning parameters
-        n_envs = int(os.environ.get("GYM_CPU_N_ENVS", "20"))
-        if n_envs < 1:
-            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
-        server_warmup_sec = float(os.environ.get("GYM_CPU_SERVER_WARMUP_SEC", "3.0"))
-        n_steps_per_env = int(os.environ.get("GYM_CPU_TRAIN_STEPS_PER_ENV", "256"))  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = int(os.environ.get("GYM_CPU_TRAIN_BATCH_SIZE", "512"))  # should be a factor of (n_steps_per_env * n_envs)
-        total_steps = 30000000
-        learning_rate = float(os.environ.get("GYM_CPU_TRAIN_LR", "3e-4"))
-        folder_name = f'Walk_R{self.robot_type}'
-        model_path = f'./scripts/gyms/logs/{folder_name}/'
-
-        print(f"Model path: {model_path}")
-        print(f"Using {n_envs} parallel environments")
-
-        # --------------------------------------- Run algorithm
-        def init_env(i_env, monitor=False):
-            def thunk():
-                env = WalkEnv(self.ip, self.server_p + i_env)
-                if monitor:
-                    env = Monitor(env)
-                return env
-
-            return thunk
-
-        server_log_dir = os.path.join(model_path, "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1, no_render=True, no_realtime=True)  # include 1 extra server for testing
-
-        # Wait for servers to start
-        print(f"Starting {n_envs + 1} rcssservermj servers...")
-        if server_warmup_sec > 0:
-            print(f"Waiting {server_warmup_sec:.1f}s for server warmup...")
-            sleep(server_warmup_sec)
-        print("Servers started, creating environments...")
-
-        env = SubprocVecEnv([init_env(i, monitor=True) for i in range(n_envs)])
-        # Use single-process eval env to avoid extra subprocess fragility during callback evaluation.
-        eval_env = DummyVecEnv([init_env(n_envs, monitor=True)])
-
-        try:
-            # Custom policy network architecture
-            policy_kwargs = dict(
-                net_arch=dict(
-                    pi=[512, 256, 128],  # Policy network: 3 layers
-                    vf=[512, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
-            )
-
-            if "model_file" in args:  # retrain
-                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
-                                 batch_size=minibatch_size, learning_rate=learning_rate)
-            else:  # train new model
-                model = PPO(
-                    "MlpPolicy",
-                    env=env,
-                    verbose=1,
-                    n_steps=n_steps_per_env,
-                    batch_size=minibatch_size,
-                    learning_rate=learning_rate,
-                    device="cpu",
-                    policy_kwargs=policy_kwargs,
-                    ent_coef=float(os.environ.get("GYM_CPU_TRAIN_ENT_COEF", "0.05")),  # Entropy coefficient for exploration
-                    clip_range=float(os.environ.get("GYM_CPU_TRAIN_CLIP_RANGE", "0.2")),  # PPO clipping parameter
-                    gae_lambda=0.95,  # GAE lambda
-                    gamma=float(os.environ.get("GYM_CPU_TRAIN_GAMMA", "0.95")), # Discount factor
-                    # target_kl=0.03,
-                    n_epochs=int(os.environ.get("GYM_CPU_TRAIN_EPOCHS", "5")),
-                    tensorboard_log=f"./scripts/gyms/logs/{folder_name}/tensorboard/"
-                )
-
-            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
-                                          eval_freq=n_steps_per_env * 20, save_freq=n_steps_per_env * 20, eval_eps=100,
-                                          backup_env_file=__file__)
-        except KeyboardInterrupt:
-            sleep(1)  # wait for child processes
-            print("\nctrl+c pressed, aborting...\n")
-            servers.kill()
-            return
-
-        env.close()
-        eval_env.close()
-        servers.kill()
-
-    def test(self, args):
-
-        # Uses different server and monitor ports
-        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
-        os.makedirs(server_log_dir, exist_ok=True)
-        test_no_render = os.environ.get("GYM_CPU_TEST_NO_RENDER", "0") == "1"
-        test_no_realtime = os.environ.get("GYM_CPU_TEST_NO_REALTIME", "0") == "1"
-
-        server = Train_Server(
-            self.server_p - 1,
-            self.monitor_p,
-            1,
-            no_render=test_no_render,
-            no_realtime=test_no_realtime,
-        )
-        env = WalkEnv(self.ip, self.server_p - 1)
-        model = PPO.load(args["model_file"], env=env)
-
-        try:
-            self.export_model(args["model_file"], args["model_file"] + ".pkl",
-                              False)  # Export to pkl to create custom behavior
-            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
-        except KeyboardInterrupt:
-            print()
-
-        env.close()
-        server.kill()
-
-
-if __name__ == "__main__":
-    from types import SimpleNamespace
-
-    # 创建默认参数
-    script_args = SimpleNamespace(
-        args=SimpleNamespace(
-            i='127.0.0.1',  # Server IP
-            p=3100,  # Server port
-            m=3200,  # Monitor port
-            r=0,  # Robot type
-            t='Gym',  # Team name
-            u=1  # Uniform number
-        )
-    )
-
-    trainer = Train(script_args)
-
-    run_mode = os.environ.get("GYM_CPU_MODE", "train").strip().lower()
-
-    if run_mode == "test":
-        test_model_file = os.environ.get("GYM_CPU_TEST_MODEL", "scripts/gyms/logs/Walk_R0_004/best_model.zip")
-        test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Walk_R0_004/")
-        trainer.test({"model_file": test_model_file, "folder_dir": test_folder})
-    else:
-        retrain_model = os.environ.get("GYM_CPU_TRAIN_MODEL", "").strip()
-        if retrain_model:
-            trainer.train({"model_file": retrain_model})
-        else:
-            trainer.train({})

train.sh

@@ -1,136 +0,0 @@
-#!/usr/bin/env bash
-set -euo pipefail
-
-# ------------------------------
-# 资源限制配置（cgroup v2 + systemd-run）
-# ------------------------------
-# 说明：
-# 1) 这个脚本会把训练进程放进一个临时的 systemd scope 中，并施加 CPU/内存上限。
-# 2) 仅限制“本次训练进程”，不会永久改系统配置。
-# 3) 下面变量都支持“环境变量覆盖”，即你可以在命令前临时指定。
-#
-# CPU 核数基准（默认 20）：
-# 例如你的机器按 20 核预算来算，可保持默认。
-CORES="${CORES:-20}"
-# CPU 占用百分比（默认 100）：
-# 最终会与 CORES 相乘得到 CPUQuota。
-# 例：CORES=20, UTIL_PERCENT=100 -> CPUQuota=2000%（约 20 核等效）
-UTIL_PERCENT="${UTIL_PERCENT:-100}"
-CPU_QUOTA="$((CORES * UTIL_PERCENT))%"
-
-# 内存上限（默认关闭）：
-# 设为具体值（如 24G/28G）可限制训练最多占用内存；
-# 设为 0/none/off/infinity 表示不设置 cgroup 内存上限。
-MEMORY_MAX="${MEMORY_MAX:-0}"
-
-# ------------------------------
-# 训练运行参数（由 scripts/gyms/Walk.py 读取）
-# ------------------------------
-# 运行模式：train 或 test
-GYM_CPU_MODE="${GYM_CPU_MODE:-train}"
-
-# 并行环境数量：越大通常吞吐越高，但也更容易触发 OOM 或连接不稳定。
-# 默认使用更稳妥的 12，确认稳定后再升到 16/20。
-GYM_CPU_N_ENVS="${GYM_CPU_N_ENVS:-12}"
-# 服务器预热时间（秒）：
-# 在批量拉起 rcssserver 后等待一段时间，再创建 SubprocVecEnv，
-# 可降低 ConnectionReset/EOFError 概率。
-GYM_CPU_SERVER_WARMUP_SEC="${GYM_CPU_SERVER_WARMUP_SEC:-10}"
-
-# 训练专用参数
-GYM_CPU_TRAIN_STEPS_PER_ENV="${GYM_CPU_TRAIN_STEPS_PER_ENV:-256}"
-GYM_CPU_TRAIN_BATCH_SIZE="${GYM_CPU_TRAIN_BATCH_SIZE:-512}"
-GYM_CPU_TRAIN_LR="${GYM_CPU_TRAIN_LR:-1e-4}"
-GYM_CPU_TRAIN_ENT_COEF="${GYM_CPU_TRAIN_ENT_COEF:-0.03}"
-GYM_CPU_TRAIN_CLIP_RANGE="${GYM_CPU_TRAIN_CLIP_RANGE:-0.13}"
-GYM_CPU_TRAIN_GAMMA="${GYM_CPU_TRAIN_GAMMA:-0.95}"
-GYM_CPU_TRAIN_EPOCHS="${GYM_CPU_TRAIN_EPOCHS:-5}"
-GYM_CPU_TRAIN_MODEL="${GYM_CPU_TRAIN_MODEL:-}"
-
-# 测试专用参数
-GYM_CPU_TEST_MODEL="${GYM_CPU_TEST_MODEL:-scripts/gyms/logs/Walk_R0_004/best_model.zip}"
-GYM_CPU_TEST_FOLDER="${GYM_CPU_TEST_FOLDER:-scripts/gyms/logs/Walk_R0_004/}"
-# 测试默认实时且显示画面：默认均为 0
-# 设为 1 表示关闭对应能力
-GYM_CPU_TEST_NO_RENDER="${GYM_CPU_TEST_NO_RENDER:-0}"
-GYM_CPU_TEST_NO_REALTIME="${GYM_CPU_TEST_NO_REALTIME:-0}"
-
-# Python 解释器选择策略：
-# 1) 优先使用你手动传入的 PYTHON_BIN
-# 2) 其次用当前激活 conda 环境（CONDA_PREFIX/bin/python）
-# 3) 再回退到默认 mujoco 环境路径
-# 4) 最后尝试系统 python / python3
-DEFAULT_PYTHON="/home/solren/Downloads/Anaconda/envs/mujoco/bin/python"
-CONDA_PYTHON="${CONDA_PREFIX:-}/bin/python"
-
-# 安全保护：不要用 sudo 运行。
-# 原因：sudo 可能导致 conda 环境与用户会话环境不一致，
-# 会引发 python 路径丢失、systemd --user 会话不可见等问题。
-if [[ "${EUID}" -eq 0 ]]; then
-	echo "Do not run this script with sudo; run as your normal user in conda env 'mujoco'."
-	exit 1
-fi
-
-# 解析最终使用的 Python 可执行文件。
-if [[ -n "${PYTHON_BIN:-}" ]]; then
-	PYTHON_EXEC="${PYTHON_BIN}"
-elif [[ -n "${CONDA_PREFIX:-}" && -x "${CONDA_PYTHON}" ]]; then
-	PYTHON_EXEC="${CONDA_PYTHON}"
-elif [[ -x "${DEFAULT_PYTHON}" ]]; then
-	PYTHON_EXEC="${DEFAULT_PYTHON}"
-elif command -v python >/dev/null 2>&1; then
-	PYTHON_EXEC="$(command -v python)"
-elif command -v python3 >/dev/null 2>&1; then
-	PYTHON_EXEC="$(command -v python3)"
-else
-	echo "No Python executable found. Set PYTHON_BIN=/abs/path/to/python and retry."
-	exit 1
-fi
-
-# 脚本所在目录（绝对路径），便于后续定位模块/相对路径。
-SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
-
-# 打印当前生效配置，方便排障和复现实验。
-echo "Starting training with limits: CPU=${CPU_QUOTA}, Memory=${MEMORY_MAX}"
-echo "Mode: ${GYM_CPU_MODE}"
-echo "Runtime knobs: GYM_CPU_N_ENVS=${GYM_CPU_N_ENVS}, GYM_CPU_SERVER_WARMUP_SEC=${GYM_CPU_SERVER_WARMUP_SEC}"
-echo "Using Python: ${PYTHON_EXEC}"
-if [[ -n "${CONDA_DEFAULT_ENV:-}" ]]; then
-	echo "Detected conda env: ${CONDA_DEFAULT_ENV}"
-fi
-
-SYSTEMD_PROPS=("-p" "CPUQuota=${CPU_QUOTA}")
-case "${MEMORY_MAX,,}" in
-	0|none|off|infinity)
-		echo "MemoryMax is disabled for this run (no cgroup memory cap)."
-		;;
-	*)
-		SYSTEMD_PROPS+=("-p" "MemoryMax=${MEMORY_MAX}")
-		;;
-esac
-
-# 使用 systemd-run --user --scope 启动“受限资源”的训练进程：
-# - CPUQuota: 总 CPU 配额
-# - MemoryMax: 最大内存
-# - env ...  : 显式传递训练参数到 Python 进程
-# - python -m scripts.gyms.Walk: 以模块方式启动训练入口
-systemd-run --user --scope \
-	"${SYSTEMD_PROPS[@]}" \
-	env \
-	GYM_CPU_MODE="${GYM_CPU_MODE}" \
-	GYM_CPU_N_ENVS="${GYM_CPU_N_ENVS}" \
-	GYM_CPU_SERVER_WARMUP_SEC="${GYM_CPU_SERVER_WARMUP_SEC}" \
-	GYM_CPU_TRAIN_STEPS_PER_ENV="${GYM_CPU_TRAIN_STEPS_PER_ENV}" \
-	GYM_CPU_TRAIN_BATCH_SIZE="${GYM_CPU_TRAIN_BATCH_SIZE}" \
-	GYM_CPU_TRAIN_LR="${GYM_CPU_TRAIN_LR}" \
-	GYM_CPU_TRAIN_ENT_COEF="${GYM_CPU_TRAIN_ENT_COEF}" \
-	GYM_CPU_TRAIN_CLIP_RANGE="${GYM_CPU_TRAIN_CLIP_RANGE}" \
-	GYM_CPU_TRAIN_GAMMA="${GYM_CPU_TRAIN_GAMMA}" \
-	GYM_CPU_TRAIN_EPOCHS="${GYM_CPU_TRAIN_EPOCHS}" \
-	GYM_CPU_TRAIN_MODEL="${GYM_CPU_TRAIN_MODEL}" \
-	GYM_CPU_TEST_MODEL="${GYM_CPU_TEST_MODEL}" \
-	GYM_CPU_TEST_FOLDER="${GYM_CPU_TEST_FOLDER}" \
-	GYM_CPU_TEST_NO_RENDER="${GYM_CPU_TEST_NO_RENDER}" \
-	GYM_CPU_TEST_NO_REALTIME="${GYM_CPU_TEST_NO_REALTIME}" \
-	"${PYTHON_EXEC}" "-m" "scripts.gyms.Walk"
-

world/world.py

@@ -47,7 +47,6 @@ class World:
         self.their_team_players: list[OtherRobot] = [OtherRobot(is_teammate=False) for _ in
                                                      range(self.MAX_PLAYERS_PER_TEAM)]
         self.field: Field = self.__initialize_field(field_name=field_name)
-        self.WORLD_STEPTIME: float = 0.005  # Time step of the world in seconds
 
     def update(self) -> None:
         """