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