Merge remote-tracking branch 'origin/master'

.gitignore

@@ -10,3 +10,9 @@ poetry.toml
 **/log/
 *.spec
 dist/
+*.zip
+*.csv
+*.json
+*.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(
+        radian_joint_positions = np.deg2rad(list(robot.motor_positions.values()))
-            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
+        radian_joint_speeds = np.deg2rad(list(robot.motor_speeds.values()))
-        )
-        radian_joint_speeds = np.deg2rad(
-            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
-        )
 
         qpos_qvel_previous_action = np.vstack(
             (

communication/server.py

@@ -1,5 +1,7 @@
 import logging
+import os
 import socket
+import time
 from select import select
 from communication.world_parser import WorldParser
 
@@ -10,15 +12,32 @@ class Server:
     def __init__(self, host: str, port: int, world_parser: WorldParser):
         self.world_parser: WorldParser = world_parser
         self.__host: str = host
-        self.__port: str = port
+        self.__port: int = port
-        self.__socket: socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+        self.__socket: socket.socket = self._create_socket()
-        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))
@@ -27,12 +46,19 @@ 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:
-        self.__socket.close()
+        try:
-        self.__socket.shutdown(socket.SHUT_RDWR)
+            self.__socket.shutdown(socket.SHUT_RDWR)
+        except OSError:
+            pass
+        try:
+            self.__socket.close()
+        except OSError:
+            pass
 
     def send_immediate(self, msg: str) -> None:
         """
@@ -85,6 +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)
@@ -100,6 +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 +1,15 @@
 import subprocess
 import os
+import time
+import threading
 
 
 class Server():
-    def __init__(self, first_server_p, first_monitor_p, n_servers) -> None:
+    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
             self.check_running_servers(psutil, first_server_p, first_monitor_p, n_servers)
@@ -13,25 +19,93 @@ 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)
 
-            server_cmd = f"{cmd} --aport {port} --mport {mport} --no-render  --no-realtime"
+        if self.WATCHDOG_ENABLED:
+            self._watchdog_thread = threading.Thread(target=self._watchdog_loop, daemon=True)
+            self._watchdog_thread.start()
 
-            self.rcss_processes.append(
+    def _spawn_server(self, port, mport, cmd, render_arg, realtime_arg):
-                subprocess.Popen(
+        server_cmd = f"{cmd} -c {port} -m {mport} {render_arg} {realtime_arg}".strip()
-                    server_cmd.split(),
+
-                    stdout=subprocess.DEVNULL,
+        proc = subprocess.Popen(
-                    stderr=subprocess.STDOUT,
+            server_cmd.split(),
-                    start_new_session=True
+            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}"
             )
 
+        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
@@ -66,6 +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

@@ -171,8 +171,8 @@ class Train_Base():
             ep_reward += reward
             ep_length += 1
 
-            if enable_FPS_control:  # control simulation speed (using non blocking user input)
+            # if enable_FPS_control:  # control simulation speed (using non blocking user input)
-                self.control_fps(select.select([sys.stdin], [], [], 0)[0])
+            #     self.control_fps(select.select([sys.stdin], [], [], 0)[0])
 
             if done:
                 obs, _ = env.reset()

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

@@ -1,606 +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 = 24
-        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.reference_joint_nominal_position = self.joint_nominal_position.copy()
-
-        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.previous_action = 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.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):
-        velocity = current_pos - previous_pos
-        velocity_magnitude = np.linalg.norm(velocity)
-        direction_to_target = self.target_position - current_pos
-        prev_direction_to_target = self.target_position - previous_pos
-        distance_to_target = np.linalg.norm(direction_to_target)
-        prev_distance_to_target = np.linalg.norm(prev_direction_to_target)
-
-        progress_reward = np.clip((prev_distance_to_target - distance_to_target) * 30.0, -2.0, 4.0)
-
-        velocity_in_m_per_sec = velocity_magnitude / 0.05
-        speed_reward = np.clip(velocity_in_m_per_sec * 1.5, 0.0, 1.5)
-
-        if velocity_magnitude > 1e-4 and distance_to_target > 1e-4:
-            directional_alignment = np.dot(velocity, direction_to_target) / (velocity_magnitude * distance_to_target)
-            directional_alignment = np.clip(directional_alignment, -1.0, 1.0)
-            direction_reward = max(0.0, directional_alignment)
-        else:
-            direction_reward = 0.0
-
-        alive_bonus = 0.05
-
-        height = self.Player.world.global_position[2]
-        if 0.45 <= height <= 1.2:
-            height_reward = 1.5
-        else:
-            height_reward = -6.0
-
-        motionless_penalty = -1.5 if velocity_magnitude < 0.003 else 0.0
-
-        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
-
-        action_magnitude = np.linalg.norm(action[11:])
-        action_penalty = -0.08 * action_magnitude
-        tilt_penalty = -0.2 * np.linalg.norm(self.Player.robot.gyroscope[:2]) / 100.0
-
-        return (
-                progress_reward
-                + speed_reward
-                + direction_reward
-                + alive_bonus
-                + height_reward
-                + motionless_penalty
-                + waypoint_bonus
-                + action_penalty
-                + tilt_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()
-
-        # 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 = 8  # 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 = 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=[256, 256, 128],  # Policy network: 3 layers
-                    vf=[256, 256, 128]  # Value network: 3 layers
-                ),
-                activation_fn=__import__('torch.nn', fromlist=['ReLU']).ReLU,
-            )
-
-            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, log_dir=server_log_dir)
-        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_000/model_245760_steps.zip"})