Revert "improve training speed and add speed constrain"

This reverts commit 648cf32e9c.
This commit is contained in:
2026-03-13 08:43:28 -04:00
parent 648cf32e9c
commit 3a42120857
4 changed files with 40 additions and 78 deletions

View File

@@ -72,37 +72,37 @@ class Server:
self.commit(msg)
self.send()
def receive(self):
while True:
def receive(self) -> None:
"""
Receive the next message from the TCP/IP socket and updates world
"""
# Receive message length information
if (
self.__socket.recv_into(
self.__rcv_buffer, nbytes=4, flags=socket.MSG_WAITALL
) != 4
)
!= 4
):
raise ConnectionResetError
msg_size = int.from_bytes(self.__rcv_buffer[:4], byteorder="big", signed=False)
# Ensure receive buffer is large enough to hold the message
if msg_size > self.__rcv_buffer_size:
self.__rcv_buffer_size = msg_size
self.__rcv_buffer = bytearray(self.__rcv_buffer_size)
# Receive message with the specified length
if (
self.__socket.recv_into(
self.__rcv_buffer, nbytes=msg_size, flags=socket.MSG_WAITALL
) != msg_size
)
!= msg_size
):
raise ConnectionResetError
self.world_parser.parse(
message=self.__rcv_buffer[:msg_size].decode()
)
# 如果socket没有更多数据就退出
if len(select([self.__socket], [], [], 0.0)[0]) == 0:
break
self.world_parser.parse(message=self.__rcv_buffer[:msg_size].decode())
def commit_beam(self, pos2d: list, rotation: float) -> None:
assert len(pos2d) == 2

View File

@@ -18,18 +18,9 @@ class Server():
# makes it easier to kill test servers without affecting train servers
cmd = "rcssservermj"
for i in range(n_servers):
port = first_server_p + i
mport = first_monitor_p + i
server_cmd = f"{cmd} --aport {port} --mport {mport} --no-render --no-realtime"
self.rcss_processes.append(
subprocess.Popen(
server_cmd.split(),
stdout=subprocess.DEVNULL,
stderr=subprocess.STDOUT,
start_new_session=True
)
subprocess.Popen((f"{cmd} --aport {first_server_p+i} --mport {first_monitor_p+i}").split(),
stdout=subprocess.DEVNULL, stderr=subprocess.STDOUT, start_new_session=True)
)
def check_running_servers(self, psutil, first_server_p, first_monitor_p, n_servers):

View File

@@ -46,7 +46,7 @@ class Train_Base():
@staticmethod
def prompt_user_for_model(self):
gyms_logs_path = "./scripts/gyms/logs/"
gyms_logs_path = "./mujococodebase/scripts/gyms/logs/"
folders = [f for f in listdir(gyms_logs_path) if isdir(join(gyms_logs_path, f))]
folders.sort(key=lambda f: os.path.getmtime(join(gyms_logs_path, f)), reverse=True) # sort by modification date

View File

@@ -1,7 +1,6 @@
import os
import numpy as np
import math
import time
from time import sleep
from random import random
from random import uniform
@@ -56,17 +55,6 @@ class WalkEnv(gym.Env):
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
@@ -153,7 +141,7 @@ class WalkEnv(gym.Env):
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
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})"
)
@@ -272,17 +260,6 @@ class WalkEnv(gym.Env):
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
@@ -345,12 +322,12 @@ class WalkEnv(gym.Env):
# 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):
for _ in range(20):
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
if finished_count >= 2: # 假设需要连续2次完成才算成功
break
# neutral_joint_positions = np.deg2rad(
@@ -516,10 +493,8 @@ class Train(Train_Base):
#--------------------------------------- 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)
minibatch_size = 64 # 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}'
@@ -534,8 +509,6 @@ class Train(Train_Base):
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
@@ -574,7 +547,7 @@ class Train(Train_Base):
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__ )
model_path = self.learn_model( model, total_steps, model_path, eval_env=eval_env, eval_freq=n_steps_per_env*20, save_freq=n_steps_per_env*20, backup_env_file=__file__ )
except KeyboardInterrupt:
sleep(1) # wait for child processes
print("\nctrl+c pressed, aborting...\n")
@@ -589,9 +562,7 @@ class Train(Train_Base):
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 )
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 )
@@ -621,4 +592,4 @@ if __name__ == "__main__":
)
trainer = Train(script_args)
trainer.train({"model_file": "scripts/gyms/logs/Walk_R0_000/model_245760_steps.zip"})
trainer.train({})