change model and policy

This reverts commit 648cf32e9c.

.gitignore

@@ -10,9 +10,3 @@ poetry.toml
 **/log/
 *.spec
 dist/
-*.zip
-*.csv
-*.json
-*.xml
-*.npz
-*.pkl

behaviors/custom/reinforcement/walk/walk.py

@@ -98,8 +98,12 @@ 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(list(robot.motor_positions.values()))
-        radian_joint_speeds = np.deg2rad(list(robot.motor_speeds.values()))
+        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.vstack(
             (

communication/server.py

@@ -1,7 +1,5 @@
 import logging
-import os
 import socket
-import time
 from select import select
 from communication.world_parser import WorldParser
 
@@ -12,32 +10,15 @@ class Server:
     def __init__(self, host: str, port: int, world_parser: WorldParser):
         self.world_parser: WorldParser = world_parser
         self.__host: str = host
-        self.__port: int = port
-        self.__socket: socket.socket = self._create_socket()
+        self.__port: str = port
+        self.__socket: socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+        self.__socket.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)
         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:
-        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-        sock.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)
-        return sock
-
     def connect(self) -> None:
         logger.info("Connecting to server at %s:%d...", self.__host, self.__port)
-
-        # Always reconnect with a fresh socket object.
-        try:
-            self.__socket.close()
-        except OSError:
-            pass
-        self.__socket = self._create_socket()
-
         while True:
             try:
                 self.__socket.connect((self.__host, self.__port))
@@ -46,19 +27,12 @@ class Server:
                 logger.error(
                     "Connection refused. Make sure the server is running and listening on {self.__host}:{self.__port}."
                 )
-                time.sleep(0.05)
 
         logger.info(f"Server connection established to {self.__host}:{self.__port}.")
 
     def shutdown(self) -> None:
-        try:
-            self.__socket.shutdown(socket.SHUT_RDWR)
-        except OSError:
-            pass
-        try:
-            self.__socket.close()
-        except OSError:
-            pass
+        self.__socket.close()
+        self.__socket.shutdown(socket.SHUT_RDWR)
 
     def send_immediate(self, msg: str) -> None:
         """
@@ -111,10 +85,6 @@ 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)
@@ -130,15 +100,6 @@ 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

@@ -74,21 +74,6 @@ 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

@@ -1,15 +1,9 @@
 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:
+    def __init__(self, first_server_p, first_monitor_p, n_servers) -> None:
         try:
             import psutil
             self.check_running_servers(psutil, first_server_p, first_monitor_p, n_servers)
@@ -19,93 +13,25 @@ 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
         cmd = "rcssservermj"
-        render_arg = "--no-render" if no_render else ""
-        realtime_arg = "--no-realtime" if no_realtime else ""
         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)
 
-        if self.WATCHDOG_ENABLED:
-            self._watchdog_thread = threading.Thread(target=self._watchdog_loop, daemon=True)
-            self._watchdog_thread.start()
+            server_cmd = f"{cmd} --aport {port} --mport {mport} "
 
-    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}"
+            self.rcss_processes.append(
+                subprocess.Popen(
+                    server_cmd.split(),
+                    stdout=subprocess.DEVNULL,
+                    stderr=subprocess.STDOUT,
+                    start_new_session=True
+                )
             )
 
-        return proc
-
-    @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
-
-    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 '''
         found = False
@@ -140,9 +66,6 @@ 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/UI.py

@@ -0,0 +1,302 @@
+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

@@ -0,0 +1,640 @@
+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 = 1000
+        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.reference_joint_nominal_position = self.joint_nominal_position.copy()
+        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
+
+        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})"
+        )
+
+    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 calibrate_train_sim_flip_from_neutral(self, neutral_joint_positions):
+        updated_flip = self.train_sim_flip.copy()
+        changed = []
+
+        for idx, (reference_value, observed_value) in enumerate(
+                zip(self.reference_joint_nominal_position, neutral_joint_positions)
+        ):
+            if idx >= 10:
+                continue
+            if abs(reference_value) < 0.15 or abs(observed_value) < 0.15:
+                continue
+
+            inferred_flip = 1.0 if np.sign(reference_value) == np.sign(observed_value) else -1.0
+            if updated_flip[idx] != inferred_flip:
+                changed.append((idx, updated_flip[idx], inferred_flip))
+                updated_flip[idx] = inferred_flip
+
+        self.train_sim_flip = updated_flip
+
+        if changed:
+            self.debug_log(
+                "[FlipDebug] "
+                f"changes={[(idx, old, new) for idx, old, new in changed]}"
+            )
+
+    def is_reliable_neutral_pose(self, neutral_joint_positions):
+        leg_positions = neutral_joint_positions[11:]
+        leg_norm = float(np.linalg.norm(leg_positions))
+        leg_max = float(np.max(np.abs(leg_positions)))
+        height = float(self.Player.world.global_position[2])
+
+        reliable = (
+                leg_norm > 0.8
+                and leg_max > 0.35
+                and 0.12 < height < 0.8
+        )
+
+        return reliable, leg_norm, leg_max, height
+
+    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}"
+        )
+
+    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 = np.random.uniform(10, 20)  # randomize target distance
+        length2 = np.random.uniform(10, 20)  # randomize target distance
+        length3 = np.random.uniform(10, 20)  # 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
+
+        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=0)
+            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(10):
+            finished = self.Player.skills_manager.execute("Neutral")
+            self.sync()
+            if finished:
+                finished_count += 1
+                if finished_count >= 3:  # 假设需要连续3次完成才算成功
+                    break
+
+        # neutral_joint_positions = np.deg2rad(
+        #     [self.Player.robot.motor_positions[motor] for motor in self.Player.robot.ROBOT_MOTORS]
+        # )
+        # reliable_neutral, neutral_leg_norm, neutral_leg_max, neutral_height = self.is_reliable_neutral_pose(neutral_joint_positions)
+
+        # if self.auto_calibrate_train_sim_flip and reliable_neutral and not self.flip_calibrated_once:
+        #     self.calibrate_train_sim_flip_from_neutral(neutral_joint_positions)
+        #     self.flip_calibrated_once = True
+        # if self.calibrate_nominal_from_neutral and reliable_neutral and not self.nominal_calibrated_once:
+        #     self.joint_nominal_position = neutral_joint_positions * self.train_sim_flip
+        #     self.nominal_calibrated_once = True
+        # self.debug_log(
+        #     "[ResetDebug] "
+        #     f"neutral_pos={np.round(self.Player.world.global_position, 3).tolist()} "
+        #     f"shoulders={np.round(neutral_joint_positions[2:10], 3).tolist()} "
+        #     f"legs={np.round(neutral_joint_positions[11:], 3).tolist()} "
+        #     f"flip={self.train_sim_flip.tolist()} "
+        #     f"nominal_legs={np.round(self.joint_nominal_position[11:], 3).tolist()} "
+        #     f"calibrated_once={(self.flip_calibrated_once, self.nominal_calibrated_once)} "
+        #     f"reliable_neutral={reliable_neutral} "
+        #     f"leg_norm={neutral_leg_norm:.3f} leg_max={neutral_leg_max:.3f} height={neutral_height:.3f}"
+        # )
+
+        # reset_action_noise = np.random.uniform(-0.015, 0.015, size=(len(self.Player.robot.ROBOT_MOTORS),))
+        # self.target_joint_positions = (self.joint_nominal_position + reset_action_noise) * 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
+        #     )
+
+        # 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)
+        point1 = self.initial_position + np.array([length1, 0])
+        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, point2, point3]
+        self.target_position = self.point_list[self.waypoint_index]
+
+        return self.observe(True), {}
+
+    def render(self, mode='human', close=False):
+        return
+
+    def compute_reward(self, previous_pos, current_pos, action):
+        eps = 1e-6
+        dt = 0.05
+
+        velocity = current_pos - previous_pos
+        speed_step = float(np.linalg.norm(velocity))
+        speed = speed_step / dt
+
+        direction_to_target = self.target_position - current_pos
+        prev_direction_to_target = self.target_position - previous_pos
+        distance_to_target = float(np.linalg.norm(direction_to_target))
+        prev_distance_to_target = float(np.linalg.norm(prev_direction_to_target))
+
+        # Progress toward waypoint (secondary signal)
+        progress = prev_distance_to_target - distance_to_target
+        progress_reward = np.clip(progress * 2.0, -1.5, 2.5)
+
+        # Forward speed and lateral drift
+        forward_dir = direction_to_target / max(distance_to_target, eps)
+        forward_speed = float(np.dot(velocity, forward_dir)) / dt
+        target_speed = 1.0
+        speed_error = forward_speed - target_speed
+        speed_reward = 3.0 * math.exp(-1.5 * (speed_error ** 2))
+
+        lateral_vec = velocity - forward_dir * np.dot(velocity, forward_dir)
+        lateral_speed = float(np.linalg.norm(lateral_vec)) / dt
+        lateral_penalty = -0.6 * np.clip(lateral_speed, 0.0, 2.0)
+
+        # Heading alignment (small shaping term)
+        if speed_step > 1e-4 and distance_to_target > 1e-4:
+            directional_alignment = np.dot(velocity, direction_to_target) / (speed_step * distance_to_target)
+            directional_alignment = float(np.clip(directional_alignment, -1.0, 1.0))
+            direction_reward = max(0.0, directional_alignment) * 0.3
+        else:
+            direction_reward = 0.0
+
+        alive_bonus = 0.05
+
+        # Height and posture
+        height = float(self.Player.world.global_position[2])
+        if 0.8 <= height <= 1.05:
+            height_reward = 1.0
+        elif 0.40 <= height <= 1.20:
+            height_reward = -1.0
+        else:
+            height_reward = -6.0
+
+        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]))
+        posture_penalty = -2.2 * (tilt_mag ** 2)
+
+        motionless_penalty = -1.5 if speed < 0.1 else 0.0
+
+        # Waypoint bonus
+        waypoint_bonus = 0.0
+        if distance_to_target < 0.5:
+            waypoint_bonus = 25.0
+            if self.waypoint_index < len(self.point_list) - 1:
+                self.waypoint_index += 1
+                self.target_position = self.point_list[self.waypoint_index]
+            else:
+                waypoint_bonus = 100.0
+                self.route_completed = True
+
+        # Effort + smoothness
+        action_magnitude = float(np.linalg.norm(action[11:]))
+        action_penalty = -0.05 * action_magnitude
+        action_delta = action - self.last_action_for_reward
+        smoothness_penalty = -0.02 * float(np.linalg.norm(action_delta[11:]))
+
+        return (
+                progress_reward
+                + speed_reward
+                + lateral_penalty
+                + direction_reward
+                + alive_bonus
+                + height_reward
+                + posture_penalty
+                + motionless_penalty
+                + waypoint_bonus
+                + action_penalty
+                + smoothness_penalty
+        )
+
+    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.sync()  # run simulation step
+        self.step_counter += 1
+
+        # if 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 = 512  # 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 = 2e-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=0.01,  # Entropy coefficient for exploration
+                    # clip_range=0.2,  # PPO clipping parameter
+                    # gae_lambda=0.95,  # GAE lambda
+                    # gamma=0.99  # Discount factor
+                )
+
+            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_003/best_model.zip",
+    #               "folder_dir": "Walk_R0_003",})