774 lines
30 KiB
Python
774 lines
30 KiB
Python
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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 itertools import count
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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.reward_debug_interval_sec = 600.0
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self.reward_debug_burst_steps = 10
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self._reward_debug_last_time = time.time()
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self._reward_debug_steps_left = 0
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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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self._speed_estimate = 0.0
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self._speed_from_acc = 0.0
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self._speed_smoothing = 0.85
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self._fallback_dt = 0.02
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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, # 0: Head_yaw (he1)
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0.0, # 1: Head_pitch (he2)
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0.0, # 2: Left_Shoulder_Pitch (lae1)
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0.0, # 3: Left_Shoulder_Roll (lae2)
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0.0, # 4: Left_Elbow_Pitch (lae3)
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0.0, # 5: Left_Elbow_Yaw (lae4)
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0.0, # 6: Right_Shoulder_Pitch (rae1)
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0.0, # 7: Right_Shoulder_Roll (rae2)
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0.0, # 8: Right_Elbow_Pitch (rae3)
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0.0, # 9: Right_Elbow_Yaw (rae4)
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0.0, # 10: Waist (te1)
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0.0, # 11: Left_Hip_Pitch (lle1)
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0.0, # 12: Left_Hip_Roll (lle2)
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1.0, # 13: Left_Hip_Yaw (lle3)
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0.0, # 14: Left_Knee_Pitch (lle4)
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0.0, # 15: Left_Ankle_Pitch (lle5)
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0.0, # 16: Left_Ankle_Roll (lle6)
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0.0, # 17: Right_Hip_Pitch (rle1)
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0.0, # 18: Right_Hip_Roll (rle2)
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1.0, # 19: Right_Hip_Yaw (rle3)
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0.0, # 20: Right_Knee_Pitch (rle4)
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0.0, # 21: Right_Ankle_Pitch (rle5)
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0.0, # 22: Right_Ankle_Roll (rle6)
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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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# Encourage a minimum lateral stance so the policy avoids feet overlap.
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self.min_stance_rad = 0.10
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# Small reset perturbations for robustness training.
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self.enable_reset_perturb = False
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self.reset_beam_yaw_range_deg = 180.0
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self.reset_target_bearing_range_deg = 0.0
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self.reset_target_distance_min = 3.0
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self.reset_target_distance_max = 5.0
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if self.reset_target_distance_min > self.reset_target_distance_max:
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self.reset_target_distance_min, self.reset_target_distance_max = (
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self.reset_target_distance_max,
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self.reset_target_distance_min,
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)
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self.reset_joint_noise_rad = 0.025
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self.reset_perturb_steps = 4
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self.reset_recover_steps = 8
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self.reward_smoothness_scale = 0.06
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self.reward_smoothness_cap = 0.45
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self.reward_forward_stability_gate = 0.35
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self.reward_forward_tilt_hard_threshold = 0.50
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self.reward_forward_tilt_hard_scale = 0.20
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self.reward_head_toward_bonus = 1.0
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self.turn_stationary_radius = 0.2
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self.turn_stationary_penalty_scale = 3.0
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self.stationary_start_steps = 20
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self.stationary_step_eps = 0.015
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self.stationary_penalty_scale = 1.2
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self.train_stage = "walk"
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self.in_place_radius = 0.18
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self.in_place_center_reward_scale = 0.60
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self.in_place_drift_penalty_scale = 1.20
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self.waypoint_reach_distance = 0.3
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self.num_waypoints = 1
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self.exploration_start_steps = 80
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self.exploration_scale = 0.08
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self.exploration_cap = 0.25
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self.exploration_target_novelty = 1.0
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self.exploration_sigma = 0.7
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self.reward_stride_swing_scale = 0.20
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self.reward_stride_phase_scale = 0.18
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self.reward_knee_drive_scale = 0.10
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self.reward_knee_lift_scale = 0.12
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self.reward_knee_lift_target = 0.95
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self.reward_knee_lift_shortfall_scale = 0.20
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self.reward_knee_overbend_threshold = 0.60
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self.reward_knee_overbend_scale = 0.35
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self.reward_hip_lift_scale = 0.12
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self.reward_hip_lift_target = 0.80
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self.reward_knee_alternate_scale = 0.10
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self.reward_knee_bilateral_scale = 0.16
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self.reward_single_leg_penalty_scale = 0.22
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self.reward_knee_phase_switch_scale = 0.14
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self.knee_phase_deadband = 0.10
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self.knee_phase_min_interval = 18
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self.knee_phase_target_interval = 22
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self.knee_phase_fast_switch_penalty_scale = 0.10
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self.knee_phase_max_hold_frames = 28
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self.knee_phase_hold_penalty_scale = 0.18
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self.reward_stride_cap = 0.80
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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.action_history_len = 50
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self.prev_action_history = np.zeros((self.action_history_len, self.no_of_actions), dtype=np.float32)
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self.history_idx = 0
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self.previous_pos = np.array([0.0, 0.0]) # Track previous position
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self.last_yaw_error = None
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self.prev_knee_balance = 0.0
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self.prev_knee_phase_sign = 0
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self.knee_phase_frames_since_switch = 0
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self.knee_phase_hold_frames = 0
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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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@staticmethod
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def _wrap_to_pi(angle_rad: float) -> float:
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return (angle_rad + math.pi) % (2.0 * math.pi) - math.pi
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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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])
|
||
|
|
|
||
|
|
observation = np.clip(observation, -10.0, 10.0)
|
||
|
|
return observation.astype(np.float32)
|
||
|
|
|
||
|
|
def sync(self):
|
||
|
|
''' Run a single simulation step '''
|
||
|
|
self._safe_receive_world_update(retries=1)
|
||
|
|
self.Player.robot.commit_motor_targets_pd()
|
||
|
|
self.Player.server.send()
|
||
|
|
if self._target_dt > 0.0:
|
||
|
|
now = time.time()
|
||
|
|
if self._last_sync_time is None:
|
||
|
|
self._last_sync_time = now
|
||
|
|
return
|
||
|
|
elapsed = now - self._last_sync_time
|
||
|
|
remaining = self._target_dt - elapsed
|
||
|
|
if remaining > 0.0:
|
||
|
|
time.sleep(remaining)
|
||
|
|
now = time.time()
|
||
|
|
self._last_sync_time = now
|
||
|
|
|
||
|
|
def debug_joint_status(self):
|
||
|
|
robot = self.Player.robot
|
||
|
|
actual_joint_positions = np.deg2rad(
|
||
|
|
[robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
|
||
|
|
)
|
||
|
|
target_joint_positions = getattr(
|
||
|
|
self,
|
||
|
|
'target_joint_positions',
|
||
|
|
np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
|
||
|
|
)
|
||
|
|
joint_error = actual_joint_positions - target_joint_positions
|
||
|
|
leg_slice = slice(11, None)
|
||
|
|
|
||
|
|
self.debug_log(
|
||
|
|
"[WalkDebug] "
|
||
|
|
f"step={self.step_counter} "
|
||
|
|
f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
|
||
|
|
f"target_xy={np.round(self.target_position, 3).tolist()} "
|
||
|
|
f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
|
||
|
|
f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
|
||
|
|
f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
|
||
|
|
f"fallen={self.Player.world.global_position[2] < 0.3}"
|
||
|
|
)
|
||
|
|
print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
|
||
|
|
|
||
|
|
def reset(self, seed=None, options=None):
|
||
|
|
'''
|
||
|
|
Reset and stabilize the robot
|
||
|
|
Note: for some behaviors it would be better to reduce stabilization or add noise
|
||
|
|
'''
|
||
|
|
r = self.Player.robot
|
||
|
|
super().reset(seed=seed)
|
||
|
|
if seed is not None:
|
||
|
|
np.random.seed(seed)
|
||
|
|
|
||
|
|
target_distance = np.random.uniform(self.reset_target_distance_min, self.reset_target_distance_max)
|
||
|
|
target_bearing_deg = np.random.uniform(-self.reset_target_bearing_range_deg, self.reset_target_bearing_range_deg)
|
||
|
|
|
||
|
|
self.step_counter = 0
|
||
|
|
self.waypoint_index = 0
|
||
|
|
self.route_completed = False
|
||
|
|
self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
|
||
|
|
self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
|
||
|
|
self.prev_action_history.fill(0.0)
|
||
|
|
self.history_idx = 0
|
||
|
|
self.previous_pos = np.array([0.0, 0.0]) # Initialize for first step
|
||
|
|
self.last_yaw_error = None
|
||
|
|
self.prev_knee_balance = 0.0
|
||
|
|
self.prev_knee_phase_sign = 0
|
||
|
|
self.knee_phase_frames_since_switch = 0
|
||
|
|
self.knee_phase_hold_frames = 0
|
||
|
|
self.walk_cycle_step = 0
|
||
|
|
self._reward_debug_steps_left = 0
|
||
|
|
self._speed_estimate = 0.0
|
||
|
|
self._speed_from_acc = 0.0
|
||
|
|
|
||
|
|
# 随机 beam 目标位置和朝向，增加训练多样性
|
||
|
|
beam_x = (random() - 0.5) * 10
|
||
|
|
beam_y = (random() - 0.5) * 10
|
||
|
|
beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
|
||
|
|
|
||
|
|
for _ in range(5):
|
||
|
|
self._safe_receive_world_update(retries=2)
|
||
|
|
self.Player.robot.commit_motor_targets_pd()
|
||
|
|
self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
|
||
|
|
self.Player.server.send()
|
||
|
|
|
||
|
|
# 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
|
||
|
|
finished_count = 0
|
||
|
|
for _ in range(50):
|
||
|
|
finished = self.Player.skills_manager.execute("Neutral")
|
||
|
|
self.sync()
|
||
|
|
if finished:
|
||
|
|
finished_count += 1
|
||
|
|
if finished_count >= 20: # 假设需要连续20次完成才算成功
|
||
|
|
break
|
||
|
|
|
||
|
|
if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
|
||
|
|
perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
|
||
|
|
# Perturb waist + lower body only (10:), keep head/arms stable.
|
||
|
|
perturb_action[10:] = np.random.uniform(
|
||
|
|
-self.reset_joint_noise_rad,
|
||
|
|
self.reset_joint_noise_rad,
|
||
|
|
size=(self.no_of_actions - 10,)
|
||
|
|
)
|
||
|
|
|
||
|
|
for _ in range(self.reset_perturb_steps):
|
||
|
|
target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
|
||
|
|
for idx, target in enumerate(target_joint_positions):
|
||
|
|
r.set_motor_target_position(
|
||
|
|
r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
|
||
|
|
)
|
||
|
|
self.sync()
|
||
|
|
|
||
|
|
for i in range(self.reset_recover_steps):
|
||
|
|
# Linearly fade perturbation to help policy start from near-neutral.
|
||
|
|
alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
|
||
|
|
target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
|
||
|
|
for idx, target in enumerate(target_joint_positions):
|
||
|
|
r.set_motor_target_position(
|
||
|
|
r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
|
||
|
|
)
|
||
|
|
self.sync()
|
||
|
|
|
||
|
|
# memory variables
|
||
|
|
self.sync()
|
||
|
|
self.initial_position = np.array(self.Player.world.global_position[:2])
|
||
|
|
self.previous_pos = self.initial_position.copy() # Critical: set to actual position
|
||
|
|
self.act = np.zeros(self.no_of_actions, np.float32)
|
||
|
|
# Generate multiple waypoints along a path
|
||
|
|
heading_deg = float(r.global_orientation_euler[2])
|
||
|
|
self.point_list = []
|
||
|
|
current_point = self.initial_position.copy()
|
||
|
|
|
||
|
|
for i in range(self.num_waypoints):
|
||
|
|
# Each waypoint is placed further along the path
|
||
|
|
target_distance_wp = np.random.uniform(self.reset_target_distance_min, self.reset_target_distance_max)
|
||
|
|
target_bearing_deg_wp = np.random.uniform(-self.reset_target_bearing_range_deg, self.reset_target_bearing_range_deg)
|
||
|
|
|
||
|
|
target_offset = MathOps.rotate_2d_vec(
|
||
|
|
np.array([target_distance_wp, 0.0]),
|
||
|
|
heading_deg + target_bearing_deg_wp,
|
||
|
|
is_rad=False,
|
||
|
|
)
|
||
|
|
next_point = current_point + target_offset
|
||
|
|
self.point_list.append(next_point)
|
||
|
|
current_point = next_point.copy()
|
||
|
|
|
||
|
|
self.target_position = self.point_list[self.waypoint_index]
|
||
|
|
if self.train_stage == "in_place":
|
||
|
|
self.target_position = self.initial_position.copy()
|
||
|
|
self.initial_height = self.Player.world.global_position[2]
|
||
|
|
|
||
|
|
return self.observe(True), {}
|
||
|
|
|
||
|
|
def render(self, mode='human', close=False):
|
||
|
|
return
|
||
|
|
|
||
|
|
|
||
|
|
def compute_reward(self, previous_pos, current_pos, action):
|
||
|
|
height = float(self.Player.world.global_position[2])
|
||
|
|
|
||
|
|
is_fallen = height < 0.55
|
||
|
|
if is_fallen:
|
||
|
|
return -20.0
|
||
|
|
|
||
|
|
prev_dist_to_target = float(np.linalg.norm(self.target_position - previous_pos))
|
||
|
|
curr_dist_to_target = float(np.linalg.norm(self.target_position - current_pos))
|
||
|
|
dist_delta = prev_dist_to_target - curr_dist_to_target
|
||
|
|
|
||
|
|
# Forward-progress reward (distance delta) with anti-stuck shaping.
|
||
|
|
progress_reward = 22.0 * dist_delta
|
||
|
|
survival_reward = 0.02
|
||
|
|
smoothness_penalty = -0.015 * float(np.linalg.norm(action - self.last_action_for_reward))
|
||
|
|
step_displacement = float(np.linalg.norm(current_pos - previous_pos))
|
||
|
|
if self.step_counter > 30 and step_displacement < 0.006:
|
||
|
|
idle_penalty = -0.06
|
||
|
|
else:
|
||
|
|
idle_penalty = 0.0
|
||
|
|
|
||
|
|
total = progress_reward + survival_reward + smoothness_penalty + idle_penalty
|
||
|
|
|
||
|
|
now = time.time()
|
||
|
|
if self.reward_debug_interval_sec > 0 and now - self._reward_debug_last_time >= self.reward_debug_interval_sec:
|
||
|
|
self._reward_debug_last_time = now
|
||
|
|
self._reward_debug_steps_left = max(1, self.reward_debug_burst_steps)
|
||
|
|
|
||
|
|
if self._reward_debug_steps_left > 0:
|
||
|
|
self._reward_debug_steps_left -= 1
|
||
|
|
self.debug_log(
|
||
|
|
f"progress_reward:{progress_reward:.4f},"
|
||
|
|
f"survival_reward:{survival_reward:.4f},"
|
||
|
|
f"smoothness_penalty:{smoothness_penalty:.4f},"
|
||
|
|
f"idle_penalty:{idle_penalty:.4f},"
|
||
|
|
f"total:{total:.4f}"
|
||
|
|
)
|
||
|
|
return total
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
def step(self, action):
|
||
|
|
|
||
|
|
r = self.Player.robot
|
||
|
|
max_action_delta = 0.5# Limit how much the action can change from the previous step to encourage smoother motions.
|
||
|
|
if self.previous_action is not None:
|
||
|
|
action = np.clip(action, self.previous_action - max_action_delta, self.previous_action + max_action_delta)
|
||
|
|
action[0:2] = 0
|
||
|
|
action[3] = 4
|
||
|
|
action[7] = -4
|
||
|
|
action[2] = 0
|
||
|
|
action[6] = 0
|
||
|
|
action[4] = 0
|
||
|
|
action[5] = -5
|
||
|
|
action[8] = 0
|
||
|
|
action[9] = 5
|
||
|
|
action[10] = 0
|
||
|
|
action[11] = np.clip(action[11], -6, 6)
|
||
|
|
action[17] = np.clip(action[17], -6, 6)
|
||
|
|
# action[11] = 1
|
||
|
|
# action[17] = 1
|
||
|
|
# action[12] = -0.01
|
||
|
|
# action[18] = 0.01
|
||
|
|
# action[13] = -1.0
|
||
|
|
# action[19] = 1.0
|
||
|
|
self.previous_action = action.copy()
|
||
|
|
|
||
|
|
self.target_joint_positions = (
|
||
|
|
# self.joint_nominal_position +
|
||
|
|
self.scaling_factor * action
|
||
|
|
)
|
||
|
|
self.target_joint_positions *= self.train_sim_flip
|
||
|
|
|
||
|
|
for idx, target in enumerate(self.target_joint_positions):
|
||
|
|
r.set_motor_target_position(
|
||
|
|
r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=60, kd=1.2
|
||
|
|
)
|
||
|
|
|
||
|
|
self.previous_action = action.copy()
|
||
|
|
|
||
|
|
self.sync() # run simulation step
|
||
|
|
self.step_counter += 1
|
||
|
|
|
||
|
|
if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
|
||
|
|
self.debug_joint_status()
|
||
|
|
|
||
|
|
current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
|
||
|
|
|
||
|
|
# Compute reward based on movement from previous step
|
||
|
|
reward = self.compute_reward(self.previous_pos, current_pos, action)
|
||
|
|
self.previous_pos = current_pos.copy()
|
||
|
|
|
||
|
|
self.prev_action_history[self.history_idx] = action.copy()
|
||
|
|
self.history_idx = (self.history_idx + 1) % self.action_history_len
|
||
|
|
|
||
|
|
self.last_action_for_reward = action.copy()
|
||
|
|
|
||
|
|
# Check if current waypoint is reached
|
||
|
|
if self.train_stage != "in_place":
|
||
|
|
dist_to_waypoint = float(np.linalg.norm(current_pos - self.target_position))
|
||
|
|
if dist_to_waypoint < self.waypoint_reach_distance:
|
||
|
|
# Move to next waypoint
|
||
|
|
self.waypoint_index += 1
|
||
|
|
if self.waypoint_index >= len(self.point_list):
|
||
|
|
# All waypoints completed
|
||
|
|
self.route_completed = True
|
||
|
|
else:
|
||
|
|
# Update target to next waypoint
|
||
|
|
self.target_position = self.point_list[self.waypoint_index]
|
||
|
|
|
||
|
|
# Fall detection and penalty
|
||
|
|
is_fallen = self.Player.world.global_position[2] < 0.55
|
||
|
|
|
||
|
|
# terminal state: the robot is falling or timeout
|
||
|
|
terminated = is_fallen or self.step_counter > 800 or self.route_completed
|
||
|
|
truncated = False
|
||
|
|
|
||
|
|
return self.observe(), reward, terminated, truncated, {}
|
||
|
|
|
||
|
|
|
||
|
|
class Train(Train_Base):
|
||
|
|
def __init__(self, script) -> None:
|
||
|
|
super().__init__(script)
|
||
|
|
|
||
|
|
def train(self, args):
|
||
|
|
|
||
|
|
# --------------------------------------- Learning parameters
|
||
|
|
n_envs = 12
|
||
|
|
server_warmup_sec = 3.0
|
||
|
|
n_steps_per_env = 256 # RolloutBuffer is of size (n_steps_per_env * n_envs)
|
||
|
|
minibatch_size = 512 # should be a factor of (n_steps_per_env * n_envs)
|
||
|
|
total_steps = 30000000
|
||
|
|
learning_rate = 2e-4
|
||
|
|
ent_coef = 0.08
|
||
|
|
clip_range = 0.2
|
||
|
|
gamma = 0.97
|
||
|
|
n_epochs = 3
|
||
|
|
enable_eval = True
|
||
|
|
monitor_train_env = False
|
||
|
|
eval_freq_mult = 30
|
||
|
|
save_freq_mult = 20
|
||
|
|
eval_eps = 3
|
||
|
|
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
|
||
|
|
|
||
|
|
env = None
|
||
|
|
eval_env = None
|
||
|
|
servers = None
|
||
|
|
try:
|
||
|
|
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=monitor_train_env) for i in range(n_envs)], start_method="spawn")
|
||
|
|
# Use single-process eval env to avoid extra subprocess fragility during callback evaluation.
|
||
|
|
if enable_eval:
|
||
|
|
eval_env = DummyVecEnv([init_env(n_envs, monitor=True)])
|
||
|
|
|
||
|
|
# 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=ent_coef, # Entropy coefficient for exploration
|
||
|
|
clip_range=clip_range, # PPO clipping parameter
|
||
|
|
gae_lambda=0.95, # GAE lambda
|
||
|
|
gamma=gamma, # Discount factor
|
||
|
|
# target_kl=0.03,
|
||
|
|
n_epochs=n_epochs,
|
||
|
|
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 * max(1, eval_freq_mult),
|
||
|
|
save_freq=n_steps_per_env * max(1, save_freq_mult),
|
||
|
|
eval_eps=max(1, eval_eps),
|
||
|
|
backup_env_file=__file__)
|
||
|
|
except KeyboardInterrupt:
|
||
|
|
sleep(1) # wait for child processes
|
||
|
|
print("\nctrl+c pressed, aborting...\n")
|
||
|
|
return
|
||
|
|
finally:
|
||
|
|
if env is not None:
|
||
|
|
env.close()
|
||
|
|
if eval_env is not None:
|
||
|
|
eval_env.close()
|
||
|
|
if servers is not None:
|
||
|
|
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 = False
|
||
|
|
test_no_realtime = False
|
||
|
|
|
||
|
|
server = Train_Server(
|
||
|
|
self.server_p - 1,
|
||
|
|
self.monitor_p,
|
||
|
|
1,
|
||
|
|
no_render=test_no_render,
|
||
|
|
no_realtime=test_no_realtime,
|
||
|
|
)
|
||
|
|
env = WalkEnv(self.ip, self.server_p - 1)
|
||
|
|
model = PPO.load(args["model_file"], env=env)
|
||
|
|
|
||
|
|
try:
|
||
|
|
self.export_model(args["model_file"], args["model_file"] + ".pkl",
|
||
|
|
False) # Export to pkl to create custom behavior
|
||
|
|
self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
|
||
|
|
except KeyboardInterrupt:
|
||
|
|
print()
|
||
|
|
|
||
|
|
env.close()
|
||
|
|
server.kill()
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
from types import SimpleNamespace
|
||
|
|
|
||
|
|
# 创建默认参数
|
||
|
|
script_args = SimpleNamespace(
|
||
|
|
args=SimpleNamespace(
|
||
|
|
i='127.0.0.1', # Server IP
|
||
|
|
p=3100, # Server port
|
||
|
|
m=3200, # Monitor port
|
||
|
|
r=0, # Robot type
|
||
|
|
t='Gym', # Team name
|
||
|
|
u=1 # Uniform number
|
||
|
|
)
|
||
|
|
)
|
||
|
|
|
||
|
|
trainer = Train(script_args)
|
||
|
|
|
||
|
|
run_mode = os.environ.get("GYM_CPU_MODE", "train").strip().lower()
|
||
|
|
|
||
|
|
if run_mode == "test":
|
||
|
|
test_model_file = os.environ.get("GYM_CPU_TEST_MODEL", "scripts/gyms/logs/Turn_R0_004/best_model.zip")
|
||
|
|
test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Turn_R0_004/")
|
||
|
|
trainer.test({"model_file": test_model_file, "folder_dir": test_folder})
|
||
|
|
else:
|
||
|
|
retrain_model = os.environ.get("GYM_CPU_TRAIN_MODEL", "").strip()
|
||
|
|
if retrain_model:
|
||
|
|
trainer.train({"model_file": retrain_model})
|
||
|
|
else:
|
||
|
|
trainer.train({})
|