853 lines
35 KiB
Python
Executable File
853 lines
35 KiB
Python
Executable File
import os
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import numpy as np
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import math
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import time
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from time import sleep
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from random import random
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from random import uniform
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from 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 = float(os.environ.get("GYM_CPU_REWARD_DEBUG_INTERVAL_SEC", "600"))
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self.reward_debug_burst_steps = int(os.environ.get("GYM_CPU_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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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 = float(os.environ.get("GYM_CPU_RESET_BEAM_YAW_RANGE_DEG", "180"))
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self.reset_target_bearing_range_deg = float(os.environ.get("GYM_CPU_RESET_TARGET_BEARING_RANGE_DEG", "45"))
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self.reset_target_distance_min = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MIN", "1.2"))
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self.reset_target_distance_max = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MAX", "2.8"))
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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 = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_SCALE", "0.06"))
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self.reward_smoothness_cap = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_CAP", "0.45"))
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self.reward_head_toward_bonus = float(os.environ.get("GYM_CPU_REWARD_HEAD_TOWARD_BONUS", "1"))
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self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
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self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
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self.previous_pos = np.array([0.0, 0.0]) # Track previous position
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self.last_yaw_error = None
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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)
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target_bearing_deg = np.random.uniform(-self.reset_target_bearing_range_deg, self.reset_target_bearing_range_deg)
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self.step_counter = 0
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self.waypoint_index = 0
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self.route_completed = False
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self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
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self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
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self.previous_pos = np.array([0.0, 0.0]) # Initialize for first step
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self.last_yaw_error = None
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self.walk_cycle_step = 0
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self._reward_debug_steps_left = 0
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# 随机 beam 目标位置和朝向，增加训练多样性
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beam_x = (random() - 0.5) * 10
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beam_y = (random() - 0.5) * 10
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beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
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for _ in range(5):
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self._safe_receive_world_update(retries=2)
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self.Player.robot.commit_motor_targets_pd()
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self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
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self.Player.server.send()
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# 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
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finished_count = 0
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for _ in range(50):
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finished = self.Player.skills_manager.execute("Neutral")
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self.sync()
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if finished:
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finished_count += 1
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if finished_count >= 20: # 假设需要连续20次完成才算成功
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break
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if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
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perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
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# Perturb waist + lower body only (10:), keep head/arms stable.
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perturb_action[10:] = np.random.uniform(
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-self.reset_joint_noise_rad,
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self.reset_joint_noise_rad,
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size=(self.no_of_actions - 10,)
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)
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for _ in range(self.reset_perturb_steps):
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target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
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for idx, target in enumerate(target_joint_positions):
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r.set_motor_target_position(
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r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
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)
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self.sync()
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for i in range(self.reset_recover_steps):
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# Linearly fade perturbation to help policy start from near-neutral.
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alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
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target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
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for idx, target in enumerate(target_joint_positions):
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r.set_motor_target_position(
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r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
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)
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self.sync()
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# memory variables
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self.sync()
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self.initial_position = np.array(self.Player.world.global_position[:2])
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self.previous_pos = self.initial_position.copy() # Critical: set to actual position
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self.act = np.zeros(self.no_of_actions, np.float32)
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# Randomize global target bearing so policy must learn to rotate toward it first.
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heading_deg = float(r.global_orientation_euler[2])
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target_offset = MathOps.rotate_2d_vec(
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np.array([target_distance, 0.0]),
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heading_deg + target_bearing_deg,
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is_rad=False,
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)
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point1 = self.initial_position + target_offset
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self.point_list = [point1]
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self.target_position = self.point_list[self.waypoint_index]
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self.initial_height = self.Player.world.global_position[2]
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return self.observe(True), {}
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def render(self, mode='human', close=False):
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return
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def compute_reward(self, previous_pos, current_pos, action):
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height = float(self.Player.world.global_position[2])
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robot = self.Player.robot
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joint_pos_rad = np.deg2rad(
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[robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
|
||
)
|
||
joint_speed_rad = np.deg2rad(
|
||
[robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
|
||
)
|
||
|
||
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]))
|
||
ang_vel = np.deg2rad(robot.gyroscope)
|
||
rp_ang_vel_mag = float(np.linalg.norm(ang_vel[:2]))
|
||
|
||
is_fallen = height < 0.55
|
||
if is_fallen:
|
||
# remain = max(0, 800 - self.step_counter)
|
||
# return -8.0 - 0.01 * remain
|
||
return -20.0
|
||
|
||
|
||
if np.linalg.norm(current_pos - previous_pos) > 0.005:
|
||
position_penalty = -3 * float(np.linalg.norm(current_pos - previous_pos))
|
||
else:
|
||
position_penalty = 0.0
|
||
|
||
|
||
# Turn-to-target shaping.
|
||
to_target = self.target_position - current_pos
|
||
dist_to_target = float(np.linalg.norm(to_target))
|
||
if dist_to_target > 1e-6:
|
||
target_yaw = math.atan2(float(to_target[1]), float(to_target[0]))
|
||
else:
|
||
target_yaw = 0.0
|
||
|
||
robot_yaw = math.radians(float(robot.global_orientation_euler[2]))
|
||
yaw_error = target_yaw - robot_yaw
|
||
|
||
# Main heading objective: face the target direction.
|
||
# heading_align_reward = 1.0 * math.cos(yaw_error)
|
||
|
||
abs_yaw_error = abs(yaw_error)
|
||
alive_bonus = 2.0 * max(0.0, 1.0 - abs_yaw_error / math.pi)
|
||
head_toward_bonus = self.reward_head_toward_bonus if abs_yaw_error < math.radians(4.0) else 0.0
|
||
|
||
if self.last_yaw_error is None:
|
||
heading_progress_reward = 0.0
|
||
else:
|
||
prev_abs_yaw_error = abs(self.last_yaw_error)
|
||
yaw_err_delta = prev_abs_yaw_error - abs_yaw_error
|
||
progress_gate = 1.0 if abs_yaw_error > math.radians(4.0) else 0.0
|
||
heading_progress_reward = 0.8 * progress_gate * yaw_err_delta
|
||
heading_progress_reward = float(np.clip(heading_progress_reward, -0.4, 0.4))
|
||
self.last_yaw_error = yaw_error
|
||
|
||
# action_penalty = -0.01 * float(np.linalg.norm(action))
|
||
smoothness_penalty = -0.05 * float(np.linalg.norm(action - self.last_action_for_reward))
|
||
|
||
posture_penalty = -0.6 * tilt_mag
|
||
# Penalize roll/pitch rotational shake but do not penalize yaw turning directly.
|
||
ang_vel_penalty = -0.06 * rp_ang_vel_mag
|
||
|
||
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_hip_pitch = float(joint_pos[11])
|
||
right_hip_pitch = float(joint_pos[17])
|
||
|
||
left_ankle_roll = float(joint_pos[16])
|
||
right_ankle_roll = float(joint_pos[22])
|
||
|
||
max_leg_roll = 0.15 # 防止劈叉姿势
|
||
split_penalty = -0.8 * 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 = 0.05 # 最小腿间距（防止贴得太近）
|
||
# 惩罚腿过分靠拢（内收）- 基于两腿间距
|
||
leg_separation = -left_hip_roll + right_hip_roll
|
||
inward_penalty = -0.25 * max(0.0, (min_leg_separation - leg_separation) / min_leg_separation)
|
||
|
||
|
||
# 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 = -0.5 * 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 = -0.3 * max(0.0, -(left_ankle_roll * right_ankle_roll))
|
||
|
||
# 分别惩罚左右大腿过度转动
|
||
max_hip_yaw = 0.3 # 最大允许的yaw角度
|
||
left_hip_yaw_penalty = -0.4 * max(0.0, abs(left_hip_yaw) - max_hip_yaw)
|
||
right_hip_yaw_penalty = -0.4 * max(0.0, abs(right_hip_yaw) - max_hip_yaw)
|
||
# 智能交叉腿惩罚：只在站立时惩罚，转身时允许交叉腿
|
||
yaw_rate = float(np.deg2rad(robot.gyroscope[2]))
|
||
yaw_rate_abs = abs(yaw_rate)
|
||
|
||
# 当转身速度较小时才惩罚交叉腿（站立状态）
|
||
cross_leg_gate = max(0.0, 1.0 - yaw_rate_abs / math.radians(8.0))
|
||
hip_yaw_cross_penalty = -1.0 * cross_leg_gate * max(0.0, -(left_hip_yaw * right_hip_yaw)) if left_hip_yaw > 0 and right_hip_yaw < 0 else 0.0
|
||
|
||
# Torso-lower-body linkage: reward coordinated turning, punish waist-only spinning.
|
||
waist_speed = abs(float(joint_speed_rad[10]))
|
||
lower_body_speed = float(np.mean(np.abs(joint_speed_rad[11:23])))
|
||
lower_body_follow_ratio = lower_body_speed / (waist_speed + 1e-4)
|
||
linkage_reward = 0.24 * min(1.0, lower_body_follow_ratio) * min(1.0, waist_speed / 1.2)
|
||
waist_only_turn_penalty = -0.20 * max(0.0, waist_speed - 1.35 * lower_body_speed)
|
||
|
||
# Extra posture linkage in yaw joints to avoid decoupled torso twist.
|
||
waist_yaw = abs(float(joint_pos_rad[10]))
|
||
hip_yaw_mean = 0.5 * (abs(float(joint_pos_rad[13])) + abs(float(joint_pos_rad[19])))
|
||
yaw_link_reward = 0.12 * math.exp(-abs(waist_yaw - hip_yaw_mean) / 0.22)
|
||
|
||
target_height = self.initial_height
|
||
height_error = height - target_height
|
||
height_error = height - target_height
|
||
|
||
height_penalty = -(math.exp(12*abs(height_error))-1) if height_error > 0.04 else 0
|
||
|
||
# # 在 compute_reward 开头附近，添加高度变化率计算
|
||
# if not hasattr(self, 'last_height'):
|
||
# self.last_height = height
|
||
# self.last_height_time = self.step_counter # 可选，用于时间间隔
|
||
# height_rate = height - self.last_height # 正为上升，负为下降
|
||
# self.last_height = height
|
||
|
||
# 惩罚高度下降（负变化率）
|
||
# height_down_penalty = -5.0 * max(0, -height_rate) # 系数可调，-height_rate 为正表示下降幅度
|
||
|
||
# # 在 compute_reward 中
|
||
# if self.step_counter > 50:
|
||
# avg_prev_action = np.mean(self.prev_action_history, axis=0)
|
||
# novelty = float(np.linalg.norm(action - avg_prev_action))
|
||
# exploration_bonus = 0.05 * novelty
|
||
# else:
|
||
# exploration_bonus = 0
|
||
|
||
# self.prev_action_history[self.history_idx] = action
|
||
# self.history_idx = (self.history_idx + 1) % 50
|
||
|
||
|
||
total = (
|
||
# progress_reward +
|
||
alive_bonus +
|
||
head_toward_bonus +
|
||
heading_progress_reward +
|
||
# lateral_penalty +
|
||
# action_penalty +
|
||
smoothness_penalty +
|
||
posture_penalty
|
||
+ ang_vel_penalty
|
||
+ height_penalty
|
||
+ ankle_roll_penalty
|
||
+ ankle_roll_cross_penalty
|
||
+ split_penalty
|
||
+ inward_penalty
|
||
# + leg_proximity_penalty
|
||
+ left_hip_yaw_penalty
|
||
+ right_hip_yaw_penalty
|
||
+ hip_yaw_cross_penalty
|
||
+ position_penalty
|
||
# + linkage_reward
|
||
# + waist_only_turn_penalty
|
||
# + yaw_link_reward
|
||
# + stance_collapse_penalty
|
||
# + hip_yaw_yaw_cross_penalty
|
||
# + stance_collapse_penalty
|
||
# + cross_leg_penalty
|
||
# + exploration_bonus
|
||
# + height_down_penalty
|
||
)
|
||
# print(height_error, height_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"height_penalty:{height_penalty:.4f},"
|
||
f"smoothness_penalty:{smoothness_penalty:.4f},"
|
||
f"posture_penalty:{posture_penalty:.4f},"
|
||
f"heading_progress_reward:{heading_progress_reward:.4f},"
|
||
# f"stance_collapse_penalty:{stance_collapse_penalty:.4f},"
|
||
# f"cross_leg_penalty:{cross_leg_penalty:.4f},"
|
||
f"ang_vel_penalty:{ang_vel_penalty:.4f},"
|
||
f"split_penalty:{split_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"hip_yaw_cross_penalty:{hip_yaw_cross_penalty:.4f},"
|
||
f"inward_penalty:{inward_penalty:.4f},"
|
||
f"position_penalty:{position_penalty:.4f},"
|
||
# f"linkage_reward:{linkage_reward:.4f},"
|
||
# f"waist_only_turn_penalty:{waist_only_turn_penalty:.4f},"
|
||
# f"yaw_link_reward:{yaw_link_reward:.4f}"
|
||
# f"leg_proximity_penalty:{leg_proximity_penalty:.4f},"
|
||
|
||
# f"stance_collapse_penalty:{stance_collapse_penalty:.4f},"
|
||
# f"hip_yaw_yaw_cross_penalty:{hip_yaw_yaw_cross_penalty:.4f},"
|
||
# f"height_down_penalty:{height_down_penalty:.4f}",
|
||
# f"exploration_bonus:{exploration_bonus:.4f}"
|
||
f"alive_bonus:{alive_bonus:.4f},"
|
||
f"abs_yaw_error:{abs_yaw_error:.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], -0.1, 0.1)
|
||
action[17] = np.clip(action[17], -0.1, 0.1)
|
||
# action[12] = -1.0
|
||
# action[18] = 1.0
|
||
# 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=110, kd=6
|
||
)
|
||
|
||
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)
|
||
|
||
if self.step_counter % 10 == 0:
|
||
self.previous_pos = current_pos.copy()
|
||
|
||
# Compute reward based on movement from previous step
|
||
reward = self.compute_reward(self.previous_pos, current_pos, action)
|
||
|
||
self.last_action_for_reward = action.copy()
|
||
|
||
# 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 = int(os.environ.get("GYM_CPU_N_ENVS", "20"))
|
||
if n_envs < 1:
|
||
raise ValueError("GYM_CPU_N_ENVS must be >= 1")
|
||
server_warmup_sec = float(os.environ.get("GYM_CPU_SERVER_WARMUP_SEC", "3.0"))
|
||
n_steps_per_env = int(os.environ.get("GYM_CPU_TRAIN_STEPS_PER_ENV", "512")) # RolloutBuffer is of size (n_steps_per_env * n_envs)
|
||
minibatch_size = int(os.environ.get("GYM_CPU_TRAIN_BATCH_SIZE", "512")) # should be a factor of (n_steps_per_env * n_envs)
|
||
total_steps = 30000000
|
||
learning_rate = float(os.environ.get("GYM_CPU_TRAIN_LR", "3e-4"))
|
||
folder_name = f'Turn_R{self.robot_type}'
|
||
model_path = f'./scripts/gyms/logs/{folder_name}/'
|
||
|
||
print(f"Model path: {model_path}")
|
||
print(f"Using {n_envs} parallel environments")
|
||
|
||
# --------------------------------------- Run algorithm
|
||
def init_env(i_env, monitor=False):
|
||
def thunk():
|
||
env = WalkEnv(self.ip, self.server_p + i_env)
|
||
if monitor:
|
||
env = Monitor(env)
|
||
return env
|
||
|
||
return thunk
|
||
|
||
server_log_dir = os.path.join(model_path, "server_logs")
|
||
os.makedirs(server_log_dir, exist_ok=True)
|
||
servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1, no_render=True, no_realtime=True) # include 1 extra server for testing
|
||
|
||
# Wait for servers to start
|
||
print(f"Starting {n_envs + 1} rcssservermj servers...")
|
||
if server_warmup_sec > 0:
|
||
print(f"Waiting {server_warmup_sec:.1f}s for server warmup...")
|
||
sleep(server_warmup_sec)
|
||
print("Servers started, creating environments...")
|
||
|
||
env = SubprocVecEnv([init_env(i, monitor=True) for i in range(n_envs)], start_method="spawn")
|
||
# Use single-process eval env to avoid extra subprocess fragility during callback evaluation.
|
||
eval_env = DummyVecEnv([init_env(n_envs, monitor=True)])
|
||
|
||
try:
|
||
# Custom policy network architecture
|
||
policy_kwargs = dict(
|
||
net_arch=dict(
|
||
pi=[512, 256, 128], # Policy network: 3 layers
|
||
vf=[512, 256, 128] # Value network: 3 layers
|
||
),
|
||
activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU,
|
||
)
|
||
|
||
if "model_file" in args: # retrain
|
||
model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
|
||
batch_size=minibatch_size, learning_rate=learning_rate)
|
||
else: # train new model
|
||
model = PPO(
|
||
"MlpPolicy",
|
||
env=env,
|
||
verbose=1,
|
||
n_steps=n_steps_per_env,
|
||
batch_size=minibatch_size,
|
||
learning_rate=learning_rate,
|
||
device="cpu",
|
||
policy_kwargs=policy_kwargs,
|
||
ent_coef=float(os.environ.get("GYM_CPU_TRAIN_ENT_COEF", "0.05")), # Entropy coefficient for exploration
|
||
clip_range=float(os.environ.get("GYM_CPU_TRAIN_CLIP_RANGE", "0.2")), # PPO clipping parameter
|
||
gae_lambda=0.95, # GAE lambda
|
||
gamma=float(os.environ.get("GYM_CPU_TRAIN_GAMMA", "0.95")), # Discount factor
|
||
# target_kl=0.03,
|
||
n_epochs=int(os.environ.get("GYM_CPU_TRAIN_EPOCHS", "5")),
|
||
tensorboard_log=f"./scripts/gyms/logs/{folder_name}/tensorboard/"
|
||
)
|
||
|
||
model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
|
||
eval_freq=n_steps_per_env * 20, save_freq=n_steps_per_env * 20, eval_eps=7,
|
||
backup_env_file=__file__)
|
||
except KeyboardInterrupt:
|
||
sleep(1) # wait for child processes
|
||
print("\nctrl+c pressed, aborting...\n")
|
||
servers.kill()
|
||
return
|
||
|
||
env.close()
|
||
eval_env.close()
|
||
servers.kill()
|
||
|
||
def test(self, args):
|
||
|
||
# Uses different server and monitor ports
|
||
server_log_dir = os.path.join(args["folder_dir"], "server_logs")
|
||
os.makedirs(server_log_dir, exist_ok=True)
|
||
test_no_render = os.environ.get("GYM_CPU_TEST_NO_RENDER", "0") == "1"
|
||
test_no_realtime = os.environ.get("GYM_CPU_TEST_NO_REALTIME", "0") == "1"
|
||
|
||
server = Train_Server(
|
||
self.server_p - 1,
|
||
self.monitor_p,
|
||
1,
|
||
no_render=test_no_render,
|
||
no_realtime=test_no_realtime,
|
||
)
|
||
env = WalkEnv(self.ip, self.server_p - 1)
|
||
model = PPO.load(args["model_file"], env=env)
|
||
|
||
try:
|
||
self.export_model(args["model_file"], args["model_file"] + ".pkl",
|
||
False) # Export to pkl to create custom behavior
|
||
self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
|
||
except KeyboardInterrupt:
|
||
print()
|
||
|
||
env.close()
|
||
server.kill()
|
||
|
||
|
||
if __name__ == "__main__":
|
||
from types import SimpleNamespace
|
||
|
||
# 创建默认参数
|
||
script_args = SimpleNamespace(
|
||
args=SimpleNamespace(
|
||
i='127.0.0.1', # Server IP
|
||
p=3100, # Server port
|
||
m=3200, # Monitor port
|
||
r=0, # Robot type
|
||
t='Gym', # Team name
|
||
u=1 # Uniform number
|
||
)
|
||
)
|
||
|
||
trainer = Train(script_args)
|
||
|
||
run_mode = os.environ.get("GYM_CPU_MODE", "train").strip().lower()
|
||
|
||
if run_mode == "test":
|
||
test_model_file = os.environ.get("GYM_CPU_TEST_MODEL", "scripts/gyms/logs/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({}) |