From 387336b35a61d0dfd0bd0b0577602c7892f3490f Mon Sep 17 00:00:00 2001 From: xxh Date: Sat, 11 Apr 2026 01:31:51 -0400 Subject: [PATCH] loosen action constraints to allow more exploration, encourage active/varied knee motions early in training without dominating progress reward. --- scripts/commons/Server.py | 2 +- scripts/gyms/Walk.py | 121 +++- scripts/gyms/logs/Walk_R0_013/Walk.py | 774 -------------------------- 3 files changed, 98 insertions(+), 799 deletions(-) delete mode 100755 scripts/gyms/logs/Walk_R0_013/Walk.py diff --git a/scripts/commons/Server.py b/scripts/commons/Server.py index 665f2b3..279e626 100644 --- a/scripts/commons/Server.py +++ b/scripts/commons/Server.py @@ -7,7 +7,7 @@ import threading class Server(): WATCHDOG_ENABLED = True WATCHDOG_INTERVAL_SEC = 30.0 - WATCHDOG_RSS_MB_LIMIT = 2000.0 + WATCHDOG_RSS_MB_LIMIT = 1000.0 def __init__(self, first_server_p, first_monitor_p, n_servers, no_render=True, no_realtime=True) -> None: try: diff --git a/scripts/gyms/Walk.py b/scripts/gyms/Walk.py index 77ba876..7759489 100755 --- a/scripts/gyms/Walk.py +++ b/scripts/gyms/Walk.py @@ -164,8 +164,8 @@ class WalkEnv(gym.Env): self.enable_reset_perturb = False self.reset_beam_yaw_range_deg = 180.0 self.reset_target_bearing_range_deg = 0.0 - self.reset_target_distance_min = 3.0 - self.reset_target_distance_max = 5.0 + self.reset_target_distance_min = 5 + self.reset_target_distance_max = 10 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, @@ -218,6 +218,9 @@ class WalkEnv(gym.Env): self.knee_phase_max_hold_frames = 28 self.knee_phase_hold_penalty_scale = 0.18 self.reward_stride_cap = 0.80 + self.reward_knee_explore_scale = 0.10 + self.reward_knee_explore_delta_scale = 0.12 + self.reward_knee_explore_cap = 0.55 self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS)) self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS)) @@ -466,6 +469,7 @@ class WalkEnv(gym.Env): 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( @@ -490,26 +494,93 @@ class WalkEnv(gym.Env): def compute_reward(self, previous_pos, current_pos, action): height = float(self.Player.world.global_position[2]) - - is_fallen = height < 0.55 - if is_fallen: - return -20.0 + 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 -20.0 + + 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]) + + max_leg_roll = 1 # 防止劈叉姿势 + split_penalty = -0.6 * 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 = -0.175 * min(0.0, left_hip_roll-min_leg_separation) + -0.175 * 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 = -0.35 * 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 = -0.2 * max(0.0, -(left_ankle_roll * right_ankle_roll)) + + # 分别惩罚左右大腿过度转动 + max_hip_yaw = 1 # 最大允许的yaw角度 + left_hip_yaw_penalty = -0.25 * max(0.0, abs(left_hip_yaw) - max_hip_yaw) + right_hip_yaw_penalty = -0.25 * max(0.0, abs(right_hip_yaw) - max_hip_yaw) + # Forward-progress reward (distance delta) with anti-stuck shaping. - progress_reward = 22.0 * dist_delta - survival_reward = 0.02 - smoothness_penalty = -0.015 * float(np.linalg.norm(action - self.last_action_for_reward)) + progress_reward = 18.0 * dist_delta + survival_reward = 0.03 + smoothness_penalty = -0.025 * float(np.linalg.norm(action - self.last_action_for_reward)) step_displacement = float(np.linalg.norm(current_pos - previous_pos)) - if self.step_counter > 30 and step_displacement < 0.006: - idle_penalty = -0.06 + if self.step_counter > 30 and step_displacement < 0.008 and self.target_distance_wp > 0.3: + idle_penalty = -0.20 else: idle_penalty = 0.0 - total = progress_reward + survival_reward + smoothness_penalty + idle_penalty + # 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 + + 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 + + total = ( + progress_reward + + survival_reward + + smoothness_penalty + + idle_penalty + + split_penalty + + inward_penalty + + ankle_roll_penalty + + ankle_roll_cross_penalty + + left_hip_yaw_penalty + + right_hip_yaw_penalty + + knee_explore_reward + + arrival_bonus + ) now = time.time() if self.reward_debug_interval_sec > 0 and now - self._reward_debug_last_time >= self.reward_debug_interval_sec: @@ -523,6 +594,14 @@ class WalkEnv(gym.Env): f"survival_reward:{survival_reward:.4f}," f"smoothness_penalty:{smoothness_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"knee_explore_reward:{knee_explore_reward:.4f}," + f"arrival_bonus:{arrival_bonus:.4f}," f"total:{total:.4f}" ) return total @@ -535,16 +614,10 @@ class WalkEnv(gym.Env): 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 + # Loosen upper-body constraints: keep motion bounded but no longer hard-lock head/arms/waist. + action[0:2] = np.clip(action[0:2], -2.0, 2.0) + action[2:10] = np.clip(action[2:10], -7.0, 7.0) + action[10] = np.clip(action[10], -3.0, 3.0) action[11] = np.clip(action[11], -6, 6) action[17] = np.clip(action[17], -6, 6) # action[11] = 1 @@ -615,7 +688,7 @@ class Train(Train_Base): def train(self, args): # --------------------------------------- Learning parameters - n_envs = 12 + 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) @@ -629,7 +702,7 @@ class Train(Train_Base): monitor_train_env = False eval_freq_mult = 60 save_freq_mult = 60 - eval_eps = 3 + eval_eps = 7 folder_name = f'Walk_R{self.robot_type}' model_path = f'./scripts/gyms/logs/{folder_name}/' diff --git a/scripts/gyms/logs/Walk_R0_013/Walk.py b/scripts/gyms/logs/Walk_R0_013/Walk.py deleted file mode 100755 index ac60b30..0000000 --- a/scripts/gyms/logs/Walk_R0_013/Walk.py +++ /dev/null @@ -1,774 +0,0 @@ -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 = 600.0 - self.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 - self._speed_estimate = 0.0 - self._speed_from_acc = 0.0 - self._speed_smoothing = 0.85 - self._fallback_dt = 0.02 - 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 = 180.0 - self.reset_target_bearing_range_deg = 0.0 - self.reset_target_distance_min = 3.0 - self.reset_target_distance_max = 5.0 - 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 = 0.06 - self.reward_smoothness_cap = 0.45 - self.reward_forward_stability_gate = 0.35 - self.reward_forward_tilt_hard_threshold = 0.50 - self.reward_forward_tilt_hard_scale = 0.20 - self.reward_head_toward_bonus = 1.0 - self.turn_stationary_radius = 0.2 - self.turn_stationary_penalty_scale = 3.0 - self.stationary_start_steps = 20 - self.stationary_step_eps = 0.015 - self.stationary_penalty_scale = 1.2 - self.train_stage = "walk" - self.in_place_radius = 0.18 - self.in_place_center_reward_scale = 0.60 - self.in_place_drift_penalty_scale = 1.20 - self.waypoint_reach_distance = 0.3 - self.num_waypoints = 1 - self.exploration_start_steps = 80 - self.exploration_scale = 0.08 - self.exploration_cap = 0.25 - self.exploration_target_novelty = 1.0 - self.exploration_sigma = 0.7 - self.reward_stride_swing_scale = 0.20 - self.reward_stride_phase_scale = 0.18 - self.reward_knee_drive_scale = 0.10 - self.reward_knee_lift_scale = 0.12 - self.reward_knee_lift_target = 0.95 - self.reward_knee_lift_shortfall_scale = 0.20 - self.reward_knee_overbend_threshold = 0.60 - self.reward_knee_overbend_scale = 0.35 - self.reward_hip_lift_scale = 0.12 - self.reward_hip_lift_target = 0.80 - self.reward_knee_alternate_scale = 0.10 - self.reward_knee_bilateral_scale = 0.16 - self.reward_single_leg_penalty_scale = 0.22 - self.reward_knee_phase_switch_scale = 0.14 - self.knee_phase_deadband = 0.10 - self.knee_phase_min_interval = 18 - self.knee_phase_target_interval = 22 - self.knee_phase_fast_switch_penalty_scale = 0.10 - self.knee_phase_max_hold_frames = 28 - self.knee_phase_hold_penalty_scale = 0.18 - self.reward_stride_cap = 0.80 - - 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.action_history_len = 50 - self.prev_action_history = np.zeros((self.action_history_len, self.no_of_actions), dtype=np.float32) - self.history_idx = 0 - self.previous_pos = np.array([0.0, 0.0]) # Track previous position - 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.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.prev_action_history.fill(0.0) - self.history_idx = 0 - self.previous_pos = np.array([0.0, 0.0]) # Initialize for first step - self.last_yaw_error = None - self.prev_knee_balance = 0.0 - self.prev_knee_phase_sign = 0 - self.knee_phase_frames_since_switch = 0 - self.knee_phase_hold_frames = 0 - self.walk_cycle_step = 0 - self._reward_debug_steps_left = 0 - self._speed_estimate = 0.0 - self._speed_from_acc = 0.0 - - # 随机 beam 目标位置和朝向，增加训练多样性 - beam_x = (random() - 0.5) * 10 - beam_y = (random() - 0.5) * 10 - beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg) - - for _ in range(5): - self._safe_receive_world_update(retries=2) - self.Player.robot.commit_motor_targets_pd() - self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw) - self.Player.server.send() - - # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立 - finished_count = 0 - for _ in range(50): - finished = self.Player.skills_manager.execute("Neutral") - self.sync() - if finished: - finished_count += 1 - if finished_count >= 20: # 假设需要连续20次完成才算成功 - break - - if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0: - perturb_action = np.zeros(self.no_of_actions, dtype=np.float32) - # Perturb waist + lower body only (10:), keep head/arms stable. - perturb_action[10:] = np.random.uniform( - -self.reset_joint_noise_rad, - self.reset_joint_noise_rad, - size=(self.no_of_actions - 10,) - ) - - for _ in range(self.reset_perturb_steps): - target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip - for idx, target in enumerate(target_joint_positions): - r.set_motor_target_position( - r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6 - ) - self.sync() - - for i in range(self.reset_recover_steps): - # Linearly fade perturbation to help policy start from near-neutral. - alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps) - target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip - for idx, target in enumerate(target_joint_positions): - r.set_motor_target_position( - r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6 - ) - self.sync() - - # memory variables - self.sync() - self.initial_position = np.array(self.Player.world.global_position[:2]) - self.previous_pos = self.initial_position.copy() # Critical: set to actual position - self.act = np.zeros(self.no_of_actions, np.float32) - # Generate multiple waypoints along a path - heading_deg = float(r.global_orientation_euler[2]) - self.point_list = [] - current_point = self.initial_position.copy() - - for i in range(self.num_waypoints): - # Each waypoint is placed further along the path - target_distance_wp = np.random.uniform(self.reset_target_distance_min, self.reset_target_distance_max) - target_bearing_deg_wp = np.random.uniform(-self.reset_target_bearing_range_deg, self.reset_target_bearing_range_deg) - - target_offset = MathOps.rotate_2d_vec( - np.array([target_distance_wp, 0.0]), - heading_deg + target_bearing_deg_wp, - is_rad=False, - ) - next_point = current_point + target_offset - self.point_list.append(next_point) - current_point = next_point.copy() - - self.target_position = self.point_list[self.waypoint_index] - if self.train_stage == "in_place": - self.target_position = self.initial_position.copy() - self.initial_height = self.Player.world.global_position[2] - - return self.observe(True), {} - - def render(self, mode='human', close=False): - return - - - def compute_reward(self, previous_pos, current_pos, action): - height = float(self.Player.world.global_position[2]) - - is_fallen = height < 0.55 - if is_fallen: - return -20.0 - - prev_dist_to_target = float(np.linalg.norm(self.target_position - previous_pos)) - curr_dist_to_target = float(np.linalg.norm(self.target_position - current_pos)) - dist_delta = prev_dist_to_target - curr_dist_to_target - - # Forward-progress reward (distance delta) with anti-stuck shaping. - progress_reward = 22.0 * dist_delta - survival_reward = 0.02 - smoothness_penalty = -0.015 * float(np.linalg.norm(action - self.last_action_for_reward)) - step_displacement = float(np.linalg.norm(current_pos - previous_pos)) - if self.step_counter > 30 and step_displacement < 0.006: - idle_penalty = -0.06 - else: - idle_penalty = 0.0 - - total = progress_reward + survival_reward + smoothness_penalty + idle_penalty - - now = time.time() - if self.reward_debug_interval_sec > 0 and now - self._reward_debug_last_time >= self.reward_debug_interval_sec: - self._reward_debug_last_time = now - self._reward_debug_steps_left = max(1, self.reward_debug_burst_steps) - - if self._reward_debug_steps_left > 0: - self._reward_debug_steps_left -= 1 - self.debug_log( - f"progress_reward:{progress_reward:.4f}," - f"survival_reward:{survival_reward:.4f}," - f"smoothness_penalty:{smoothness_penalty:.4f}," - f"idle_penalty:{idle_penalty:.4f}," - f"total:{total:.4f}" - ) - return total - - - - def step(self, action): - - r = self.Player.robot - max_action_delta = 0.5# Limit how much the action can change from the previous step to encourage smoother motions. - if self.previous_action is not None: - action = np.clip(action, self.previous_action - max_action_delta, self.previous_action + max_action_delta) - action[0:2] = 0 - action[3] = 4 - action[7] = -4 - action[2] = 0 - action[6] = 0 - action[4] = 0 - action[5] = -5 - action[8] = 0 - action[9] = 5 - action[10] = 0 - action[11] = np.clip(action[11], -6, 6) - action[17] = np.clip(action[17], -6, 6) - # action[11] = 1 - # action[17] = 1 - # action[12] = -0.01 - # action[18] = 0.01 - # action[13] = -1.0 - # action[19] = 1.0 - self.previous_action = action.copy() - - self.target_joint_positions = ( - # self.joint_nominal_position + - self.scaling_factor * action - ) - self.target_joint_positions *= self.train_sim_flip - - for idx, target in enumerate(self.target_joint_positions): - r.set_motor_target_position( - r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=60, kd=1.2 - ) - - self.previous_action = action.copy() - - self.sync() # run simulation step - self.step_counter += 1 - - if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0: - self.debug_joint_status() - - current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32) - - # Compute reward based on movement from previous step - reward = self.compute_reward(self.previous_pos, current_pos, action) - self.previous_pos = current_pos.copy() - - self.prev_action_history[self.history_idx] = action.copy() - self.history_idx = (self.history_idx + 1) % self.action_history_len - - self.last_action_for_reward = action.copy() - - # Check if current waypoint is reached - if self.train_stage != "in_place": - dist_to_waypoint = float(np.linalg.norm(current_pos - self.target_position)) - if dist_to_waypoint < self.waypoint_reach_distance: - # Move to next waypoint - self.waypoint_index += 1 - if self.waypoint_index >= len(self.point_list): - # All waypoints completed - self.route_completed = True - else: - # Update target to next waypoint - self.target_position = self.point_list[self.waypoint_index] - - # Fall detection and penalty - is_fallen = self.Player.world.global_position[2] < 0.55 - - # terminal state: the robot is falling or timeout - terminated = is_fallen or self.step_counter > 800 or self.route_completed - truncated = False - - return self.observe(), reward, terminated, truncated, {} - - -class Train(Train_Base): - def __init__(self, script) -> None: - super().__init__(script) - - def train(self, args): - - # --------------------------------------- Learning parameters - n_envs = 12 - server_warmup_sec = 3.0 - n_steps_per_env = 256 # RolloutBuffer is of size (n_steps_per_env * n_envs) - minibatch_size = 512 # should be a factor of (n_steps_per_env * n_envs) - total_steps = 30000000 - learning_rate = 2e-4 - ent_coef = 0.08 - clip_range = 0.2 - gamma = 0.97 - n_epochs = 3 - enable_eval = True - monitor_train_env = False - eval_freq_mult = 30 - save_freq_mult = 20 - eval_eps = 3 - folder_name = f'Walk_R{self.robot_type}' - model_path = f'./scripts/gyms/logs/{folder_name}/' - - print(f"Model path: {model_path}") - print(f"Using {n_envs} parallel environments") - - # --------------------------------------- Run algorithm - def init_env(i_env, monitor=False): - def thunk(): - env = WalkEnv(self.ip, self.server_p + i_env) - if monitor: - env = Monitor(env) - return env - - return thunk - - env = None - eval_env = None - servers = None - try: - server_log_dir = os.path.join(model_path, "server_logs") - os.makedirs(server_log_dir, exist_ok=True) - servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1, no_render=True, no_realtime=True) # include 1 extra server for testing - - # Wait for servers to start - print(f"Starting {n_envs + 1} rcssservermj servers...") - if server_warmup_sec > 0: - print(f"Waiting {server_warmup_sec:.1f}s for server warmup...") - sleep(server_warmup_sec) - print("Servers started, creating environments...") - - env = SubprocVecEnv([init_env(i, monitor=monitor_train_env) for i in range(n_envs)], start_method="spawn") - # Use single-process eval env to avoid extra subprocess fragility during callback evaluation. - if enable_eval: - eval_env = DummyVecEnv([init_env(n_envs, monitor=True)]) - - # Custom policy network architecture - policy_kwargs = dict( - net_arch=dict( - pi=[512, 256, 128], # Policy network: 3 layers - vf=[512, 256, 128] # Value network: 3 layers - ), - activation_fn=__import__('torch.nn', fromlist=['ELU']).ELU, - ) - - if "model_file" in args: # retrain - model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env, - batch_size=minibatch_size, learning_rate=learning_rate) - else: # train new model - model = PPO( - "MlpPolicy", - env=env, - verbose=1, - n_steps=n_steps_per_env, - batch_size=minibatch_size, - learning_rate=learning_rate, - device="cpu", - policy_kwargs=policy_kwargs, - ent_coef=ent_coef, # Entropy coefficient for exploration - clip_range=clip_range, # PPO clipping parameter - gae_lambda=0.95, # GAE lambda - gamma=gamma, # Discount factor - # target_kl=0.03, - n_epochs=n_epochs, - tensorboard_log=f"./scripts/gyms/logs/{folder_name}/tensorboard/" - ) - - model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env, - eval_freq=n_steps_per_env * max(1, eval_freq_mult), - save_freq=n_steps_per_env * max(1, save_freq_mult), - eval_eps=max(1, eval_eps), - backup_env_file=__file__) - except KeyboardInterrupt: - sleep(1) # wait for child processes - print("\nctrl+c pressed, aborting...\n") - return - finally: - if env is not None: - env.close() - if eval_env is not None: - eval_env.close() - if servers is not None: - servers.kill() - - def test(self, args): - - # Uses different server and monitor ports - server_log_dir = os.path.join(args["folder_dir"], "server_logs") - os.makedirs(server_log_dir, exist_ok=True) - test_no_render = False - test_no_realtime = False - - server = Train_Server( - self.server_p - 1, - self.monitor_p, - 1, - no_render=test_no_render, - no_realtime=test_no_realtime, - ) - env = WalkEnv(self.ip, self.server_p - 1) - model = PPO.load(args["model_file"], env=env) - - try: - self.export_model(args["model_file"], args["model_file"] + ".pkl", - False) # Export to pkl to create custom behavior - self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"]) - except KeyboardInterrupt: - print() - - env.close() - server.kill() - - -if __name__ == "__main__": - from types import SimpleNamespace - - # 创建默认参数 - script_args = SimpleNamespace( - args=SimpleNamespace( - i='127.0.0.1', # Server IP - p=3100, # Server port - m=3200, # Monitor port - r=0, # Robot type - t='Gym', # Team name - u=1 # Uniform number - ) - ) - - trainer = Train(script_args) - - run_mode = os.environ.get("GYM_CPU_MODE", "train").strip().lower() - - if run_mode == "test": - test_model_file = os.environ.get("GYM_CPU_TEST_MODEL", "scripts/gyms/logs/Turn_R0_004/best_model.zip") - test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Turn_R0_004/") - trainer.test({"model_file": test_model_file, "folder_dir": test_folder}) - else: - retrain_model = os.environ.get("GYM_CPU_TRAIN_MODEL", "").strip() - if retrain_model: - trainer.train({"model_file": retrain_model}) - else: - trainer.train({}) \ No newline at end of file