update Walk.py and update models for Walk gym
This commit is contained in:
968
scripts/gyms/logs/Walk_version_0.10/Walk.py
Executable file
968
scripts/gyms/logs/Walk_version_0.10/Walk.py
Executable file
@@ -0,0 +1,968 @@
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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, TD3, DDPG, SAC, A2C
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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._prev_accelerometer = np.zeros(3, dtype=np.float32)
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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 = 0.5
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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 = 5
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self.reset_target_distance_max = 10
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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 = 0.03
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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 = 40
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self.exploration_scale = 0.012
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self.exploration_cap = 0.2
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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 = 0.15
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self.reward_knee_lift_shortfall_scale = 0.05
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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.reward_knee_explore_scale = 0.03
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self.reward_knee_explore_delta_scale = 0.03
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self.reward_knee_explore_cap = 0.10
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self.reward_hip_pitch_explore_scale = 0.07
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self.reward_hip_pitch_explore_delta_scale = 0.07
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self.reward_hip_pitch_explore_cap = 0.10
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self.reward_progress_scale = 18
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self.reward_survival_scale = 0.5
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self.reward_idle_penalty_scale = 0.6
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self.reward_accel_penalty_scale = 0.08
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self.reward_accel_penalty_cap = 0.40
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self.reward_accel_abs_limit = 13.5
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self.reward_accel_abs_penalty_scale = 0.05
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self.reward_accel_abs_penalty_cap = 0.40
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self.reward_heading_align_scale = 0.28
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self.reward_heading_error_scale = 0.05
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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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])
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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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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
|
||||
self._prev_accelerometer = np.array(
|
||||
getattr(self.Player.robot, "accelerometer", np.zeros(3)),
|
||||
dtype=np.float32,
|
||||
)
|
||||
|
||||
# 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)
|
||||
self.target_distance_wp = target_distance_wp
|
||||
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])
|
||||
robot = self.Player.robot
|
||||
|
||||
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
|
||||
|
||||
is_fallen = height < 0.55
|
||||
if is_fallen:
|
||||
return -2.0
|
||||
|
||||
joint_pos = np.deg2rad(
|
||||
[robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
|
||||
) * self.train_sim_flip
|
||||
left_hip_roll = -float(joint_pos[12])
|
||||
right_hip_roll = float(joint_pos[18])
|
||||
|
||||
left_ankle_roll = -float(joint_pos[16])
|
||||
right_ankle_roll = float(joint_pos[22])
|
||||
left_knee_flex = abs(float(joint_pos[14]))
|
||||
right_knee_flex = abs(float(joint_pos[20]))
|
||||
avg_knee_flex = 0.5 * (left_knee_flex + right_knee_flex)
|
||||
|
||||
max_leg_roll = 0.5 # 防止劈叉姿势
|
||||
split_penalty = -0.12 * max(0.0, (left_hip_roll + right_hip_roll - 2 * max_leg_roll) / max_leg_roll)
|
||||
left_hip_yaw = -float(joint_pos[13])
|
||||
right_hip_yaw = float(joint_pos[19])
|
||||
|
||||
min_leg_separation = 0.04 # 最小腿间距（防止贴得太近）
|
||||
inward_penalty = 0.3 * min(0.0, (left_hip_roll-min_leg_separation)) + 0.3 * min(0.0, (right_hip_roll-min_leg_separation)) # 惩罚左右腿过度内扣
|
||||
|
||||
|
||||
# 脚踝roll角度检测：防止过度外翻或内翻
|
||||
max_ankle_roll = 0.15 # 最大允许的脚踝roll角度
|
||||
|
||||
# 惩罚脚踝过度外翻/内翻（绝对值过大）
|
||||
ankle_roll_penalty = -0.12 * max(0.0, (abs(left_ankle_roll) + abs(right_ankle_roll) - 2 * max_ankle_roll) / max_ankle_roll)
|
||||
|
||||
# 惩罚两脚踝roll方向相反（不稳定姿势）
|
||||
ankle_roll_cross_penalty = -0.12 * max(0.0, -(left_ankle_roll * right_ankle_roll))
|
||||
|
||||
# 分别惩罚左右大腿过度转动
|
||||
max_hip_yaw = 0.2 # 最大允许的yaw角度
|
||||
left_hip_yaw_penalty = -0.6 * max(0.0, abs(left_hip_yaw) - max_hip_yaw)
|
||||
right_hip_yaw_penalty = -0.6 * max(0.0, abs(right_hip_yaw) - max_hip_yaw)
|
||||
|
||||
target_vec = self.target_position - current_pos
|
||||
target_dist = float(np.linalg.norm(target_vec))
|
||||
if target_dist > 1e-6:
|
||||
target_heading = math.atan2(float(target_vec[1]), float(target_vec[0]))
|
||||
robot_heading = math.radians(float(robot.global_orientation_euler[2]))
|
||||
heading_error = self._wrap_to_pi(target_heading - robot_heading)
|
||||
heading_align_reward = self.reward_heading_align_scale * math.cos(heading_error)
|
||||
heading_error_penalty = -self.reward_heading_error_scale * abs(heading_error)
|
||||
else:
|
||||
heading_align_reward = 0.0
|
||||
heading_error_penalty = 0.0
|
||||
|
||||
# Forward-progress reward (distance delta) with anti-stuck shaping.
|
||||
progress_reward = self.reward_progress_scale * dist_delta
|
||||
survival_reward = self.reward_survival_scale
|
||||
smoothness_penalty = -self.reward_smoothness_scale * float(np.linalg.norm(action - self.last_action_for_reward))
|
||||
step_displacement = float(np.linalg.norm(current_pos - previous_pos))
|
||||
accel_signal = 0.0
|
||||
accel_source = "imu_delta"
|
||||
accel_now = np.array(getattr(robot, "accelerometer", np.zeros(3)), dtype=np.float32)
|
||||
if accel_now.shape[0] >= 3:
|
||||
# Use IMU acceleration delta to reduce gravity bias and punish abrupt bursts.
|
||||
accel_signal = float(np.linalg.norm(accel_now[:3] - self._prev_accelerometer[:3]))
|
||||
self._prev_accelerometer = accel_now
|
||||
accel_penalty = -min(
|
||||
self.reward_accel_penalty_cap,
|
||||
self.reward_accel_penalty_scale * accel_signal,
|
||||
)
|
||||
accel_abs = float(np.linalg.norm(accel_now[:3])) if accel_now.shape[0] >= 3 else 0.0
|
||||
accel_abs_over = max(0.0, accel_abs - self.reward_accel_abs_limit)
|
||||
accel_abs_penalty = -min(
|
||||
self.reward_accel_abs_penalty_cap,
|
||||
self.reward_accel_abs_penalty_scale * accel_abs_over,
|
||||
)
|
||||
if self.step_counter > 30 and step_displacement < 0.015 and self.target_distance_wp > 0.3:
|
||||
idle_penalty = -self.reward_idle_penalty_scale
|
||||
else:
|
||||
idle_penalty = 0.0
|
||||
|
||||
if self.step_counter > self.exploration_start_steps:
|
||||
displacement_novelty = step_displacement / max(1e-6, self.stationary_step_eps)
|
||||
exploration_bonus = min(
|
||||
self.exploration_cap,
|
||||
self.exploration_scale * max(0.0, displacement_novelty - self.exploration_target_novelty),
|
||||
)
|
||||
else:
|
||||
exploration_bonus = 0.0
|
||||
|
||||
# Encourage active/varied knee motions early in training without dominating progress reward.
|
||||
left_knee_act = float(action[14])
|
||||
right_knee_act = float(action[20])
|
||||
left_knee_delta = abs(left_knee_act - float(self.last_action_for_reward[14]))
|
||||
right_knee_delta = abs(right_knee_act - float(self.last_action_for_reward[20]))
|
||||
knee_action_mag = 0.5 * (abs(left_knee_act) + abs(right_knee_act))
|
||||
knee_action_delta = 0.5 * (left_knee_delta + right_knee_delta)
|
||||
if self.step_counter > 10:
|
||||
knee_explore_reward = min(
|
||||
self.reward_knee_explore_cap,
|
||||
self.reward_knee_explore_scale * knee_action_mag
|
||||
+ self.reward_knee_explore_delta_scale * knee_action_delta,
|
||||
)
|
||||
else:
|
||||
knee_explore_reward = 0.0
|
||||
|
||||
# Directly encourage observable knee flexion instead of only action exploration.
|
||||
knee_lift_shortfall_penalty = -self.reward_knee_lift_shortfall_scale * max(
|
||||
0.0, self.reward_knee_lift_target - avg_knee_flex
|
||||
)
|
||||
|
||||
# Encourage hip-pitch exploration to improve forward stride generation.
|
||||
left_hip_pitch_act = float(action[11])
|
||||
right_hip_pitch_act = float(action[17])
|
||||
left_hip_pitch_delta = abs(left_hip_pitch_act - float(self.last_action_for_reward[11]))
|
||||
right_hip_pitch_delta = abs(right_hip_pitch_act - float(self.last_action_for_reward[17]))
|
||||
hip_pitch_action_mag = 0.5 * (abs(left_hip_pitch_act) + abs(right_hip_pitch_act))
|
||||
hip_pitch_action_delta = 0.5 * (left_hip_pitch_delta + right_hip_pitch_delta)
|
||||
if self.step_counter > 10:
|
||||
hip_pitch_explore_reward = min(
|
||||
self.reward_hip_pitch_explore_cap,
|
||||
self.reward_hip_pitch_explore_scale * hip_pitch_action_mag
|
||||
+ self.reward_hip_pitch_explore_delta_scale * hip_pitch_action_delta,
|
||||
)
|
||||
else:
|
||||
hip_pitch_explore_reward = 0.0
|
||||
|
||||
if curr_dist_to_target < 0.3:
|
||||
arrival_bonus = self.target_distance_wp * 8 ## 奖励到达目标点
|
||||
else:
|
||||
arrival_bonus = 0.0
|
||||
|
||||
target_height = self.initial_height
|
||||
height_error = height - target_height
|
||||
height_error = height - target_height
|
||||
|
||||
height_penalty = -0.5 * (math.exp(15*abs(height_error))-1)
|
||||
|
||||
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]))
|
||||
tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
|
||||
posture_penalty = -0.6 * (tilt_mag)
|
||||
total = (
|
||||
progress_reward
|
||||
+ survival_reward
|
||||
+ smoothness_penalty
|
||||
+ accel_penalty
|
||||
+ accel_abs_penalty
|
||||
+ idle_penalty
|
||||
+ split_penalty
|
||||
+ inward_penalty
|
||||
+ ankle_roll_penalty
|
||||
+ ankle_roll_cross_penalty
|
||||
+ left_hip_yaw_penalty
|
||||
+ right_hip_yaw_penalty
|
||||
+ heading_align_reward
|
||||
+ heading_error_penalty
|
||||
# + exploration_bonus
|
||||
# + knee_explore_reward
|
||||
# + knee_lift_shortfall_penalty
|
||||
# + hip_pitch_explore_reward
|
||||
+ arrival_bonus
|
||||
+ height_penalty
|
||||
+ posture_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"accel_penalty:{accel_penalty:.4f},"
|
||||
f"accel_source:{accel_source},"
|
||||
f"accel_signal:{accel_signal:.4f},"
|
||||
f"accel_abs:{accel_abs:.4f},"
|
||||
f"accel_abs_penalty:{accel_abs_penalty:.4f},"
|
||||
f"idle_penalty:{idle_penalty:.4f},"
|
||||
f"split_penalty:{split_penalty:.4f},"
|
||||
f"inward_penalty:{inward_penalty:.4f},"
|
||||
f"ankle_roll_penalty:{ankle_roll_penalty:.4f},"
|
||||
f"ankle_roll_cross_penalty:{ankle_roll_cross_penalty:.4f},"
|
||||
f"left_hip_yaw_penalty:{left_hip_yaw_penalty:.4f},"
|
||||
f"right_hip_yaw_penalty:{right_hip_yaw_penalty:.4f},"
|
||||
f"heading_align_reward:{heading_align_reward:.4f},"
|
||||
f"heading_error_penalty:{heading_error_penalty:.4f},"
|
||||
# f"exploration_bonus:{exploration_bonus:.4f},"
|
||||
f"height_penalty:{height_penalty:.4f},"
|
||||
# f"knee_explore_reward:{knee_explore_reward:.4f},"
|
||||
f"posture_penalty:{posture_penalty:.4f},"
|
||||
# f"knee_lift_shortfall_penalty:{knee_lift_shortfall_penalty:.4f},"
|
||||
# f"hip_pitch_explore_reward:{hip_pitch_explore_reward:.4f},"
|
||||
f"arrival_bonus:{arrival_bonus:.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)
|
||||
# Loosen upper-body constraints: keep motion bounded but no longer hard-lock head/arms/waist.
|
||||
action[0:2] = 0
|
||||
action[3] = np.clip(action[3], 3, 5)
|
||||
action[7] = np.clip(action[7], -5, -3)
|
||||
action[2] = np.clip(action[2], -6, 6)
|
||||
action[6] = np.clip(action[6], -6, 6)
|
||||
action[4] = 0
|
||||
action[5] = np.clip(action[5], -8, -2)
|
||||
action[8] = 0
|
||||
action[9] = np.clip(action[9], 8, 2)
|
||||
action[10] = np.clip(action[10], -0.6, 0.6)
|
||||
# Boost knee command range so policy can produce visible knee flexion earlier.
|
||||
action[14] = np.clip(action[14], 0, 10.0)
|
||||
action[20] = np.clip(action[20], -10.0, 0)
|
||||
# action[14] = 1 # the correct left knee sign
|
||||
# action[20] = -1 # the correct right knee sign
|
||||
# action[11] = 1
|
||||
# action[17] = 1
|
||||
# action[12] = -1
|
||||
# action[18] = 1
|
||||
# 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 = 20
|
||||
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 = 90000000
|
||||
learning_rate = 2e-4
|
||||
ent_coef = 0.035
|
||||
clip_range = 0.2
|
||||
gamma = 0.97
|
||||
n_epochs = 3
|
||||
enable_eval = True
|
||||
monitor_train_env = False
|
||||
eval_freq_mult = 60
|
||||
save_freq_mult = 60
|
||||
eval_eps = 7
|
||||
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({})
|
||||
Reference in New Issue
Block a user