From 5a7952a91c4f11c9d9d8ccb16858eb9d8c605d68 Mon Sep 17 00:00:00 2001 From: xxh Date: Wed, 8 Apr 2026 08:04:34 -0400 Subject: [PATCH] update walk script --- .gitignore | 1 + scripts/gyms/Walk.py | 4 +- scripts/gyms/logs/Walk_R0_013/Walk.py | 774 ++++++++++++++++++++++++++ 3 files changed, 777 insertions(+), 2 deletions(-) create mode 100755 scripts/gyms/logs/Walk_R0_013/Walk.py diff --git a/.gitignore b/.gitignore index 036a917..ed3d9fb 100644 --- a/.gitignore +++ b/.gitignore @@ -20,3 +20,4 @@ best_model.zip *.yaml *.iml *.TXT +events.out.tfevents.* diff --git a/scripts/gyms/Walk.py b/scripts/gyms/Walk.py index ac60b30..77ba876 100755 --- a/scripts/gyms/Walk.py +++ b/scripts/gyms/Walk.py @@ -627,8 +627,8 @@ class Train(Train_Base): n_epochs = 3 enable_eval = True monitor_train_env = False - eval_freq_mult = 30 - save_freq_mult = 20 + eval_freq_mult = 60 + save_freq_mult = 60 eval_eps = 3 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 new file mode 100755 index 0000000..ac60b30 --- /dev/null +++ b/scripts/gyms/logs/Walk_R0_013/Walk.py @@ -0,0 +1,774 @@ +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