Gym_CPU/scripts/gyms/Walk.py

import os
import numpy as np
import math
import time
from time import sleep
from random import random
from random import uniform
from itertools import count

from stable_baselines3 import PPO
from stable_baselines3.common.monitor import Monitor
from stable_baselines3.common.vec_env import SubprocVecEnv, DummyVecEnv

import gymnasium as gym
from gymnasium import spaces

from scripts.commons.Train_Base import Train_Base
from scripts.commons.Server import Server as Train_Server

from agent.base_agent import Base_Agent
from utils.math_ops import MathOps

from scipy.spatial.transform import Rotation as R

'''
Objective:
Learn how to run forward using step primitive
----------
- class Basic_Run: implements an OpenAI custom gym
- class Train:  implements algorithms to train a new model or test an existing model
'''


class WalkEnv(gym.Env):
    def __init__(self, ip, server_p) -> None:

        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
        self.Player = player = Base_Agent(
            team_name="Gym",
            number=1,
            host=ip,
            port=server_p
        )
        self.robot_type = self.Player.robot
        self.step_counter = 0  # to limit episode size
        self.force_play_on = True

        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
        self.isfallen = False
        self.waypoint_index = 0
        self.route_completed = False
        self.debug_every_n_steps = 5
        self.enable_debug_joint_status = False
        self.reward_debug_interval_sec = float(os.environ.get("GYM_CPU_REWARD_DEBUG_INTERVAL_SEC", "600"))
        self.reward_debug_burst_steps = int(os.environ.get("GYM_CPU_REWARD_DEBUG_BURST_STEPS", "10"))
        self._reward_debug_last_time = time.time()
        self._reward_debug_steps_left = 0
        self.calibrate_nominal_from_neutral = True
        self.auto_calibrate_train_sim_flip = True
        self.nominal_calibrated_once = False
        self.flip_calibrated_once = False
        self._target_hz = 0.0
        self._target_dt = 0.0
        self._last_sync_time = None
        target_hz_env = 0
        if target_hz_env:
            try:
                self._target_hz = float(target_hz_env)
            except ValueError:
                self._target_hz = 0.0
        if self._target_hz > 0.0:
            self._target_dt = 1.0 / self._target_hz

        # State space
        # 原始观测大小: 78
        obs_size = 78
        self.obs = np.zeros(obs_size, np.float32)
        self.observation_space = spaces.Box(
            low=-10.0,
            high=10.0,
            shape=(obs_size,),
            dtype=np.float32
        )

        action_dim = len(self.Player.robot.ROBOT_MOTORS)
        self.no_of_actions = action_dim
        self.action_space = spaces.Box(
            low=-10.0,
            high=10.0,
            shape=(action_dim,),
            dtype=np.float32
        )

        # 中立姿态
        self.joint_nominal_position = np.array(
            [
                0.0,  # 0: Head_yaw (he1)
                0.0,  # 1: Head_pitch (he2)
                0.0,  # 2: Left_Shoulder_Pitch (lae1)
                0.0,  # 3: Left_Shoulder_Roll (lae2)
                0.0,  # 4: Left_Elbow_Pitch (lae3)
                0.0,  # 5: Left_Elbow_Yaw (lae4)
                0.0,  # 6: Right_Shoulder_Pitch (rae1)
                0.0,  # 7: Right_Shoulder_Roll (rae2)
                0.0,  # 8: Right_Elbow_Pitch (rae3)
                0.0,  # 9: Right_Elbow_Yaw (rae4)
                0.0,  # 10: Waist (te1)
                0.0,  # 11: Left_Hip_Pitch (lle1)
                0.0,  # 12: Left_Hip_Roll (lle2)
                1.0,  # 13: Left_Hip_Yaw (lle3)
                0.0,  # 14: Left_Knee_Pitch (lle4)
                0.0,  # 15: Left_Ankle_Pitch (lle5)
                0.0,  # 16: Left_Ankle_Roll (lle6)
                0.0,  # 17: Right_Hip_Pitch (rle1)
                0.0,  # 18: Right_Hip_Roll (rle2)
                1.0,  # 19: Right_Hip_Yaw (rle3)
                0.0,  # 20: Right_Knee_Pitch (rle4)
                0.0,  # 21: Right_Ankle_Pitch (rle5)
                0.0,  # 22: Right_Ankle_Roll (rle6)
            ]
        )
        self.joint_nominal_position = np.zeros(self.no_of_actions)
        self.train_sim_flip = np.array(
            [
                1.0,  # 0: Head_yaw (he1)
                -1.0,  # 1: Head_pitch (he2)
                1.0,  # 2: Left_Shoulder_Pitch (lae1)
                -1.0,  # 3: Left_Shoulder_Roll (lae2)
                -1.0,  # 4: Left_Elbow_Pitch (lae3)
                1.0,  # 5: Left_Elbow_Yaw (lae4)
                -1.0,  # 6: Right_Shoulder_Pitch (rae1)
                -1.0,  # 7: Right_Shoulder_Roll (rae2)
                1.0,  # 8: Right_Elbow_Pitch (rae3)
                1.0,  # 9: Right_Elbow_Yaw (rae4)
                1.0,  # 10: Waist (te1)
                1.0,  # 11: Left_Hip_Pitch (lle1)
                -1.0,  # 12: Left_Hip_Roll (lle2)
                -1.0,  # 13: Left_Hip_Yaw (lle3)
                1.0,  # 14: Left_Knee_Pitch (lle4)
                1.0,  # 15: Left_Ankle_Pitch (lle5)
                -1.0,  # 16: Left_Ankle_Roll (lle6)
                -1.0,  # 17: Right_Hip_Pitch (rle1)
                -1.0,  # 18: Right_Hip_Roll (rle2)
                -1.0,  # 19: Right_Hip_Yaw (rle3)
                -1.0,  # 20: Right_Knee_Pitch (rle4)
                -1.0,  # 21: Right_Ankle_Pitch (rle5)
                -1.0,  # 22: Right_Ankle_Roll (rle6)
            ]
        )

        self.scaling_factor = 0.3
        # self.scaling_factor = 1

        # Encourage a minimum lateral stance so the policy avoids feet overlap.
        self.min_stance_rad = 0.10

        # Small reset perturbations for robustness training.
        self.enable_reset_perturb = False
        self.reset_beam_yaw_range_deg = float(os.environ.get("GYM_CPU_RESET_BEAM_YAW_RANGE_DEG", "180"))
        self.reset_target_bearing_range_deg = float(os.environ.get("GYM_CPU_RESET_TARGET_BEARING_RANGE_DEG", "90"))
        self.reset_target_distance_min = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MIN", "1.2"))
        self.reset_target_distance_max = float(os.environ.get("GYM_CPU_RESET_TARGET_DISTANCE_MAX", "2.8"))
        if self.reset_target_distance_min > self.reset_target_distance_max:
            self.reset_target_distance_min, self.reset_target_distance_max = (
                self.reset_target_distance_max,
                self.reset_target_distance_min,
            )
        self.reset_joint_noise_rad = 0.025
        self.reset_perturb_steps = 4
        self.reset_recover_steps = 8

        self.reward_smoothness_scale = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_SCALE", "0.06"))
        self.reward_smoothness_cap = float(os.environ.get("GYM_CPU_REWARD_SMOOTHNESS_CAP", "0.45"))
        self.reward_head_toward_bonus = float(os.environ.get("GYM_CPU_REWARD_HEAD_TOWARD_BONUS", "1"))

        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.previous_pos = np.array([0.0, 0.0])  # Track previous position
        self.last_yaw_error = None
        self.Player.server.connect()
        # sleep(2.0)  # Longer wait for connection to establish completely
        self.Player.server.send_immediate(
            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
        )
        self.start_time = time.time()

    def _reconnect_server(self):
        try:
            self.Player.server.shutdown()
        except Exception:
            pass

        self.Player.server.connect()
        self.Player.server.send_immediate(
            f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
        )

    def _safe_receive_world_update(self, retries=1):
        last_exc = None
        for attempt in range(retries + 1):
            try:
                self.Player.server.receive()
                self.Player.world.update()
                return
            except (ConnectionResetError, OSError) as exc:
                last_exc = exc
                if attempt >= retries:
                    raise
                self._reconnect_server()
        if last_exc is not None:
            raise last_exc

    def debug_log(self, message):
        print(message)
        try:
            log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
            with open(log_path, "a", encoding="utf-8") as f:
                f.write(message + "\n")
        except OSError:
            pass

    @staticmethod
    def _wrap_to_pi(angle_rad: float) -> float:
        return (angle_rad + math.pi) % (2.0 * math.pi) - math.pi

    def observe(self, init=False):

        """获取当前观测值"""
        robot = self.Player.robot
        world = self.Player.world

        # Safety check: ensure data is available

        # 计算目标速度
        raw_target = self.target_position - world.global_position[:2]
        velocity = MathOps.rotate_2d_vec(
            raw_target,
            -robot.global_orientation_euler[2],
            is_rad=False
        )

        # 计算相对方向
        rel_orientation = MathOps.vector_angle(velocity) * 0.3
        rel_orientation = np.clip(rel_orientation, -0.25, 0.25)

        velocity = np.concatenate([velocity, np.array([rel_orientation])])
        velocity[0] = np.clip(velocity[0], -0.5, 0.5)
        velocity[1] = np.clip(velocity[1], -0.25, 0.25)

        # 关节状态
        radian_joint_positions = np.deg2rad(
            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
        )
        radian_joint_speeds = np.deg2rad(
            [robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
        )

        qpos_qvel_previous_action = np.concatenate([
            (radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
            radian_joint_speeds / 110.0 * self.train_sim_flip,
            self.previous_action / 10.0,
        ])

        # 角速度
        ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)

        # 投影的重力方向
        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]))

        # 组合观测
        observation = np.concatenate([
            qpos_qvel_previous_action,
            ang_vel,
            velocity,
            projected_gravity,
        ])

        observation = np.clip(observation, -10.0, 10.0)
        return observation.astype(np.float32)

    def sync(self):
        ''' Run a single simulation step '''
        self._safe_receive_world_update(retries=1)
        self.Player.robot.commit_motor_targets_pd()
        self.Player.server.send()
        if self._target_dt > 0.0:
            now = time.time()
            if self._last_sync_time is None:
                self._last_sync_time = now
                return
            elapsed = now - self._last_sync_time
            remaining = self._target_dt - elapsed
            if remaining > 0.0:
                time.sleep(remaining)
                now = time.time()
            self._last_sync_time = now

    def debug_joint_status(self):
        robot = self.Player.robot
        actual_joint_positions = np.deg2rad(
            [robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
        )
        target_joint_positions = getattr(
            self,
            'target_joint_positions',
            np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
        )
        joint_error = actual_joint_positions - target_joint_positions
        leg_slice = slice(11, None)

        self.debug_log(
            "[WalkDebug] "
            f"step={self.step_counter} "
            f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
            f"target_xy={np.round(self.target_position, 3).tolist()} "
            f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
            f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
            f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
            f"fallen={self.Player.world.global_position[2] < 0.3}"
        )
        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")

    def reset(self, seed=None, options=None):
        '''
        Reset and stabilize the robot
        Note: for some behaviors it would be better to reduce stabilization or add noise
        '''
        r = self.Player.robot
        super().reset(seed=seed)
        if seed is not None:
            np.random.seed(seed)

        target_distance = np.random.uniform(self.reset_target_distance_min, self.reset_target_distance_max)
        target_bearing_deg = np.random.uniform(-self.reset_target_bearing_range_deg, self.reset_target_bearing_range_deg)

        self.step_counter = 0
        self.waypoint_index = 0
        self.route_completed = False
        self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
        self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
        self.previous_pos = np.array([0.0, 0.0])  # Initialize for first step
        self.last_yaw_error = None
        self.walk_cycle_step = 0
        self._reward_debug_steps_left = 0

        # 随机 beam 目标位置和朝向，增加训练多样性
        beam_x = (random() - 0.5) * 10
        beam_y = (random() - 0.5) * 10
        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)

        for _ in range(5):
            self._safe_receive_world_update(retries=2)
            self.Player.robot.commit_motor_targets_pd()
            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
            self.Player.server.send()

        # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
        finished_count = 0
        for _ in range(50):
            finished = self.Player.skills_manager.execute("Neutral")
            self.sync()
            if finished:
                finished_count += 1
                if finished_count >= 20:  # 假设需要连续20次完成才算成功
                    break

        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
            # Perturb waist + lower body only (10:), keep head/arms stable.
            perturb_action[10:] = np.random.uniform(
                -self.reset_joint_noise_rad,
                self.reset_joint_noise_rad,
                size=(self.no_of_actions - 10,)
            )

            for _ in range(self.reset_perturb_steps):
                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
                for idx, target in enumerate(target_joint_positions):
                    r.set_motor_target_position(
                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
                    )
                self.sync()

            for i in range(self.reset_recover_steps):
                # Linearly fade perturbation to help policy start from near-neutral.
                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
                for idx, target in enumerate(target_joint_positions):
                    r.set_motor_target_position(
                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
                    )
                self.sync()

        # memory variables
        self.sync()  
        self.initial_position = np.array(self.Player.world.global_position[:2])
        self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
        self.act = np.zeros(self.no_of_actions, np.float32)
        # Randomize global target bearing so policy must learn to rotate toward it first.
        heading_deg = float(r.global_orientation_euler[2])
        target_offset = MathOps.rotate_2d_vec(
            np.array([target_distance, 0.0]),
            heading_deg + target_bearing_deg,
            is_rad=False,
        )
        point1 = self.initial_position + target_offset
        self.point_list = [point1]
        self.target_position = self.point_list[self.waypoint_index]
        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


        joint_pos_rad = np.deg2rad(
            [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 = 0.2  # 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 = 0.4  # 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}"
                )
        # print(f"abs_yaw_error:{abs_yaw_error:.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], -0.5, 0.5)
        # action[17] = np.clip(action[17], -0.5, 0.5)
        # 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=5
            )

        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({})