From 77120ecb7ba4fb2e41f4bf43369a11d92063679a Mon Sep 17 00:00:00 2001 From: xxh Date: Tue, 24 Mar 2026 06:42:06 -0400 Subject: [PATCH] train scripts history --- scripts/gyms/logs/Walk_R0_000/Walk.py | 624 ++++++++++++++++ scripts/gyms/logs/Walk_R0_001/Walk.py | 625 ++++++++++++++++ scripts/gyms/logs/Walk_R0_002/Walk.py | 625 ++++++++++++++++ scripts/gyms/logs/Walk_R0_003/Walk.py | 625 ++++++++++++++++ scripts/gyms/logs/Walk_R0_004/Walk.py | 626 ++++++++++++++++ scripts/gyms/logs/Walk_R0_005/Walk.py | 660 +++++++++++++++++ scripts/gyms/logs/Walk_R0_006/Walk.py | 679 ++++++++++++++++++ scripts/gyms/logs/Walk_R0_007/Walk.py | 679 ++++++++++++++++++ scripts/gyms/logs/Walk_R0_008/Walk.py | 704 ++++++++++++++++++ scripts/gyms/logs/Walk_R0_009/Walk.py | 705 +++++++++++++++++++ scripts/gyms/logs/Walk_R0_010/Walk.py | 705 +++++++++++++++++++ scripts/gyms/logs/stand_stable_0.1/Walk.py | 626 ++++++++++++++++ scripts/gyms/logs/stand_stable_final/Walk.py | 705 +++++++++++++++++++ 13 files changed, 8588 insertions(+) create mode 100644 scripts/gyms/logs/Walk_R0_000/Walk.py create mode 100644 scripts/gyms/logs/Walk_R0_001/Walk.py create mode 100644 scripts/gyms/logs/Walk_R0_002/Walk.py create mode 100644 scripts/gyms/logs/Walk_R0_003/Walk.py create mode 100644 scripts/gyms/logs/Walk_R0_004/Walk.py create mode 100755 scripts/gyms/logs/Walk_R0_005/Walk.py create mode 100755 scripts/gyms/logs/Walk_R0_006/Walk.py create mode 100755 scripts/gyms/logs/Walk_R0_007/Walk.py create mode 100755 scripts/gyms/logs/Walk_R0_008/Walk.py create mode 100755 scripts/gyms/logs/Walk_R0_009/Walk.py create mode 100755 scripts/gyms/logs/Walk_R0_010/Walk.py create mode 100644 scripts/gyms/logs/stand_stable_0.1/Walk.py create mode 100755 scripts/gyms/logs/stand_stable_final/Walk.py diff --git a/scripts/gyms/logs/Walk_R0_000/Walk.py b/scripts/gyms/logs/Walk_R0_000/Walk.py new file mode 100644 index 0000000..52effa6 --- /dev/null +++ b/scripts/gyms/logs/Walk_R0_000/Walk.py @@ -0,0 +1,624 @@ +import os +import numpy as np +import math +import time +from time import sleep +from random import random +from random import uniform + +from stable_baselines3 import PPO +from stable_baselines3.common.vec_env import SubprocVecEnv + +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.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=-1.0, + high=1.0, + shape=(action_dim,), + dtype=np.float32 + ) + + # 中立姿态 + self.joint_nominal_position = np.array( + [ + 0.0, + 0.0, + 0.0, + 1.4, + 0.0, + -0.4, + 0.0, + -1.4, + 0.0, + 0.4, + 0.0, + -0.4, + 0.0, + 0.0, + 0.8, + -0.4, + 0.0, + 0.4, + 0.0, + 0.0, + -0.8, + 0.4, + 0.0, + ] + ) + 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 = 0.5 + # self.scaling_factor = 1 + + # Small reset perturbations for robustness training. + self.enable_reset_perturb = False + self.reset_beam_yaw_range_deg = 180 # randomize target direction fully to encourage learning a real walk instead of a fixed gait + self.reset_joint_noise_rad = 0.035 + self.reset_perturb_steps = 5 + self.reset_recover_steps = 8 + + 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.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 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 + + 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.Player.server.receive() + self.Player.world.update() + 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) + + length1 = 2 # randomize target distance + length2 = np.random.uniform(0.6, 1) # randomize target distance + length3 = np.random.uniform(0.6, 1) # randomize target distance + angle2 = np.random.uniform(-30, 30) # randomize initial orientation + angle3 = np.random.uniform(-30, 30) # randomize target direction + + 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.walk_cycle_step = 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.Player.server.receive() + self.Player.world.update() + 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.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) + # Build target in the robot's current forward direction instead of fixed global +x. + heading_deg = float(r.global_orientation_euler[2]) + forward_offset = MathOps.rotate_2d_vec(np.array([length1, 0.0]), heading_deg, is_rad=False) + point1 = self.initial_position + forward_offset + point2 = point1 + MathOps.rotate_2d_vec(np.array([length2, 0]), angle2, is_rad=False) + point3 = point2 + MathOps.rotate_2d_vec(np.array([length3, 0]), angle3, is_rad=False) + 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]) + + orientation_quat_inv = R.from_quat(self.Player.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(self.Player.robot.gyroscope) + ang_vel_mag = float(np.linalg.norm(ang_vel)) + + # 摔倒检测（重要！） + if height < 0.3: + if time.time() - self.start_time > 1200: + self.start_time = time.time() + print("fall_penalty: -20") + return -20.0 + + + + # # 目标方向 + # to_target = self.target_position - current_pos + # dist_to_target = float(np.linalg.norm(to_target)) + # if dist_to_target < 0.5: + # return 15.0 + + # forward_dir = to_target / dist_to_target if dist_to_target > 0.1 else np.array([1.0, 0.0]) + # delta_pos = current_pos - previous_pos + # forward_step = float(np.dot(delta_pos, forward_dir)) + # lateral_step = float(np.linalg.norm(delta_pos - forward_dir * forward_step)) + + # 奖励项 + # progress_reward = 2 * forward_step + # lateral_penalty = -0.1 * lateral_step + alive_bonus = 0.1 + + # 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 = -1.0 * (tilt_mag) + # ang_vel_penalty = -0.05 * ang_vel_mag + + target_height = self.initial_height + height_error = height - target_height if abs(height - target_height) > 0.05 else 0.0 + height_penalty = -2.0 * abs(height_error) # 惩罚高度偏离，系数可调 + + # # 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 + + # lateral_penalty + + # action_penalty + + smoothness_penalty + + posture_penalty + # + ang_vel_penalty + + height_penalty + # + exploration_bonus + # + height_down_penalty + ) + if time.time() - self.start_time >= 1200: + self.start_time = time.time() + print( + # f"progress_reward:{progress_reward:.4f}", + # f"lateral_penalty:{lateral_penalty:.4f}", + # f"action_penalty:{action_penalty:.4f}"s, + f"height_penalty:{height_penalty:.4f}", + f"smoothness_penalty:{smoothness_penalty:.4f},", + f"posture_penalty:{posture_penalty:.4f}", + # f"ang_vel_penalty:{ang_vel_penalty:.4f}", + # f"height_down_penalty:{height_down_penalty:.4f}", + # f"exploration_bonus:{exploration_bonus:.4f}" + ) + + return total + + + + def step(self, action): + + r = self.Player.robot + self.previous_action = action + + 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=25, kd=0.6 + ) + + self.previous_action = action + + self.sync() # run simulation step + self.step_counter += 1 + + # 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) + + # Update previous position + self.previous_pos = current_pos.copy() + self.last_action_for_reward = action.copy() + + # Fall detection and penalty + is_fallen = self.Player.world.global_position[2] < 0.3 + + # terminal state: the robot is falling or timeout + terminated = is_fallen or self.step_counter > 800 or self.route_completed + truncated = False + + return self.observe(), reward, terminated, truncated, {} + + +class Train(Train_Base): + def __init__(self, script) -> None: + super().__init__(script) + + def train(self, args): + + # --------------------------------------- Learning parameters + n_envs = 20 # Reduced from 8 to decrease CPU/network pressure during init + if n_envs < 1: + raise ValueError("GYM_CPU_N_ENVS must be >= 1") + n_steps_per_env = 1024 # RolloutBuffer is of size (n_steps_per_env * n_envs) + minibatch_size = 128 # should be a factor of (n_steps_per_env * n_envs) + total_steps = 30000000 + learning_rate = 3e-4 + 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): + def thunk(): + return WalkEnv(self.ip, self.server_p + i_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) # include 1 extra server for testing + + # Wait for servers to start + print(f"Starting {n_envs + 1} rcssservermj servers...") + print("Servers started, creating environments...") + + env = SubprocVecEnv([init_env(i) for i in range(n_envs)]) + eval_env = SubprocVecEnv([init_env(n_envs)]) + + 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=0.005, # Entropy coefficient for exploration + # clip_range=0.13, # PPO clipping parameter + gae_lambda=0.95, # GAE lambda + gamma=0.99 , # Discount factor + # target_kl=0.03, + # n_epochs=5 + ) + + model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env, + eval_freq=n_steps_per_env * 10, save_freq=n_steps_per_env * 10, + 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) + server = Train_Server(self.server_p - 1, self.monitor_p, 1) + 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) + trainer.train({}) + # trainer.test({"model_file": "scripts/gyms/logs/Walk_R0_012/best_model.zip", + # "folder_dir": "scripts/gyms/logs/Walk_R0_012/",}) \ No newline at end of file diff --git a/scripts/gyms/logs/Walk_R0_001/Walk.py b/scripts/gyms/logs/Walk_R0_001/Walk.py new file mode 100644 index 0000000..b750fbd --- /dev/null +++ b/scripts/gyms/logs/Walk_R0_001/Walk.py @@ -0,0 +1,625 @@ +import os +import numpy as np +import math +import time +from time import sleep +from random import random +from random import uniform + +from stable_baselines3 import PPO +from stable_baselines3.common.vec_env import SubprocVecEnv + +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.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, + 0.0, + 0.0, + 1.4, + 0.0, + -0.4, + 0.0, + -1.4, + 0.0, + 0.4, + 0.0, + -0.4, + 0.0, + 0.0, + 0.8, + -0.4, + 0.0, + 0.4, + 0.0, + 0.0, + -0.8, + 0.4, + 0.0, + ] + ) + 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 = 0.1 + # self.scaling_factor = 1 + + # Small reset perturbations for robustness training. + self.enable_reset_perturb = False + self.reset_beam_yaw_range_deg = 180 # randomize target direction fully to encourage learning a real walk instead of a fixed gait + self.reset_joint_noise_rad = 0.035 + self.reset_perturb_steps = 5 + self.reset_recover_steps = 8 + + 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.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 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 + + 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.Player.server.receive() + self.Player.world.update() + 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) + + length1 = 2 # randomize target distance + length2 = np.random.uniform(0.6, 1) # randomize target distance + length3 = np.random.uniform(0.6, 1) # randomize target distance + angle2 = np.random.uniform(-30, 30) # randomize initial orientation + angle3 = np.random.uniform(-30, 30) # randomize target direction + + 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.walk_cycle_step = 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.Player.server.receive() + self.Player.world.update() + 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) + # Build target in the robot's current forward direction instead of fixed global +x. + heading_deg = float(r.global_orientation_euler[2]) + forward_offset = MathOps.rotate_2d_vec(np.array([length1, 0.0]), heading_deg, is_rad=False) + point1 = self.initial_position + forward_offset + point2 = point1 + MathOps.rotate_2d_vec(np.array([length2, 0]), angle2, is_rad=False) + point3 = point2 + MathOps.rotate_2d_vec(np.array([length3, 0]), angle3, is_rad=False) + 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]) + + orientation_quat_inv = R.from_quat(self.Player.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(self.Player.robot.gyroscope) + ang_vel_mag = float(np.linalg.norm(ang_vel)) + + is_fallen = height < 0.3 + if is_fallen: + remain = max(0, 800 - self.step_counter) + return -8.0 - 0.01 * remain + + + + # # 目标方向 + # to_target = self.target_position - current_pos + # dist_to_target = float(np.linalg.norm(to_target)) + # if dist_to_target < 0.5: + # return 15.0 + + # forward_dir = to_target / dist_to_target if dist_to_target > 0.1 else np.array([1.0, 0.0]) + # delta_pos = current_pos - previous_pos + # forward_step = float(np.dot(delta_pos, forward_dir)) + # lateral_step = float(np.linalg.norm(delta_pos - forward_dir * forward_step)) + + # 奖励项 + # progress_reward = 2 * forward_step + # lateral_penalty = -0.1 * lateral_step + alive_bonus = 0.3 + + # action_penalty = -0.01 * float(np.linalg.norm(action)) + smoothness_penalty = -0.03 * float(np.linalg.norm(action - self.last_action_for_reward)) + + posture_penalty = -1.0 * (tilt_mag) + # ang_vel_penalty = -0.05 * ang_vel_mag + + target_height = self.initial_height + height_error = height - target_height if abs(height - target_height) > 0.05 else 0.0 + height_penalty = -2.0 * abs(height_error) # 惩罚高度偏离，系数可调 + + # # 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 + + # lateral_penalty + + # action_penalty + + smoothness_penalty + + posture_penalty + # + ang_vel_penalty + + height_penalty + # + exploration_bonus + # + height_down_penalty + ) + if time.time() - self.start_time >= 1200: + self.start_time = time.time() + print( + # f"progress_reward:{progress_reward:.4f}", + # f"lateral_penalty:{lateral_penalty:.4f}", + # f"action_penalty:{action_penalty:.4f}"s, + f"height_penalty:{height_penalty:.4f}", + f"smoothness_penalty:{smoothness_penalty:.4f},", + f"posture_penalty:{posture_penalty:.4f}", + # f"ang_vel_penalty:{ang_vel_penalty:.4f}", + # f"height_down_penalty:{height_down_penalty:.4f}", + # f"exploration_bonus:{exploration_bonus:.4f}" + ) + + return total + + + + def step(self, action): + + r = self.Player.robot + self.previous_action = action + + 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=25, kd=0.6 + ) + + self.previous_action = action + + 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) + + # Update previous position + self.previous_pos = current_pos.copy() + self.last_action_for_reward = action.copy() + + # Fall detection and penalty + is_fallen = self.Player.world.global_position[2] < 0.3 + + # terminal state: the robot is falling or timeout + terminated = is_fallen or self.step_counter > 800 or self.route_completed + truncated = False + + return self.observe(), reward, terminated, truncated, {} + + +class Train(Train_Base): + def __init__(self, script) -> None: + super().__init__(script) + + def train(self, args): + + # --------------------------------------- Learning parameters + n_envs = 20 # Reduced from 8 to decrease CPU/network pressure during init + if n_envs < 1: + raise ValueError("GYM_CPU_N_ENVS must be >= 1") + n_steps_per_env = 1024 # RolloutBuffer is of size (n_steps_per_env * n_envs) + minibatch_size = 128 # should be a factor of (n_steps_per_env * n_envs) + total_steps = 30000000 + learning_rate = 3e-4 + 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): + def thunk(): + return WalkEnv(self.ip, self.server_p + i_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) # include 1 extra server for testing + + # Wait for servers to start + print(f"Starting {n_envs + 1} rcssservermj servers...") + print("Servers started, creating environments...") + + env = SubprocVecEnv([init_env(i) for i in range(n_envs)]) + eval_env = SubprocVecEnv([init_env(n_envs)]) + + 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=0.005, # Entropy coefficient for exploration + # clip_range=0.13, # PPO clipping parameter + gae_lambda=0.95, # GAE lambda + gamma=0.99 , # Discount factor + # target_kl=0.03, + # n_epochs=5 + ) + + model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env, + eval_freq=n_steps_per_env * 10, save_freq=n_steps_per_env * 10, + 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) + server = Train_Server(self.server_p - 1, self.monitor_p, 1) + 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) + trainer.train({}) + # trainer.test({"model_file": "scripts/gyms/logs/Walk_R0_000/best_model.zip", + # "folder_dir": "scripts/gyms/logs/Walk_R0_000/",}) \ No newline at end of file diff --git a/scripts/gyms/logs/Walk_R0_002/Walk.py b/scripts/gyms/logs/Walk_R0_002/Walk.py new file mode 100644 index 0000000..b750fbd --- /dev/null +++ b/scripts/gyms/logs/Walk_R0_002/Walk.py @@ -0,0 +1,625 @@ +import os +import numpy as np +import math +import time +from time import sleep +from random import random +from random import uniform + +from stable_baselines3 import PPO +from stable_baselines3.common.vec_env import SubprocVecEnv + +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.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, + 0.0, + 0.0, + 1.4, + 0.0, + -0.4, + 0.0, + -1.4, + 0.0, + 0.4, + 0.0, + -0.4, + 0.0, + 0.0, + 0.8, + -0.4, + 0.0, + 0.4, + 0.0, + 0.0, + -0.8, + 0.4, + 0.0, + ] + ) + 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 = 0.1 + # self.scaling_factor = 1 + + # Small reset perturbations for robustness training. + self.enable_reset_perturb = False + self.reset_beam_yaw_range_deg = 180 # randomize target direction fully to encourage learning a real walk instead of a fixed gait + self.reset_joint_noise_rad = 0.035 + self.reset_perturb_steps = 5 + self.reset_recover_steps = 8 + + 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.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 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 + + 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.Player.server.receive() + self.Player.world.update() + 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) + + length1 = 2 # randomize target distance + length2 = np.random.uniform(0.6, 1) # randomize target distance + length3 = np.random.uniform(0.6, 1) # randomize target distance + angle2 = np.random.uniform(-30, 30) # randomize initial orientation + angle3 = np.random.uniform(-30, 30) # randomize target direction + + 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.walk_cycle_step = 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.Player.server.receive() + self.Player.world.update() + 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) + # Build target in the robot's current forward direction instead of fixed global +x. + heading_deg = float(r.global_orientation_euler[2]) + forward_offset = MathOps.rotate_2d_vec(np.array([length1, 0.0]), heading_deg, is_rad=False) + point1 = self.initial_position + forward_offset + point2 = point1 + MathOps.rotate_2d_vec(np.array([length2, 0]), angle2, is_rad=False) + point3 = point2 + MathOps.rotate_2d_vec(np.array([length3, 0]), angle3, is_rad=False) + 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]) + + orientation_quat_inv = R.from_quat(self.Player.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(self.Player.robot.gyroscope) + ang_vel_mag = float(np.linalg.norm(ang_vel)) + + is_fallen = height < 0.3 + if is_fallen: + remain = max(0, 800 - self.step_counter) + return -8.0 - 0.01 * remain + + + + # # 目标方向 + # to_target = self.target_position - current_pos + # dist_to_target = float(np.linalg.norm(to_target)) + # if dist_to_target < 0.5: + # return 15.0 + + # forward_dir = to_target / dist_to_target if dist_to_target > 0.1 else np.array([1.0, 0.0]) + # delta_pos = current_pos - previous_pos + # forward_step = float(np.dot(delta_pos, forward_dir)) + # lateral_step = float(np.linalg.norm(delta_pos - forward_dir * forward_step)) + + # 奖励项 + # progress_reward = 2 * forward_step + # lateral_penalty = -0.1 * lateral_step + alive_bonus = 0.3 + + # action_penalty = -0.01 * float(np.linalg.norm(action)) + smoothness_penalty = -0.03 * float(np.linalg.norm(action - self.last_action_for_reward)) + + posture_penalty = -1.0 * (tilt_mag) + # ang_vel_penalty = -0.05 * ang_vel_mag + + target_height = self.initial_height + height_error = height - target_height if abs(height - target_height) > 0.05 else 0.0 + height_penalty = -2.0 * abs(height_error) # 惩罚高度偏离，系数可调 + + # # 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 + + # lateral_penalty + + # action_penalty + + smoothness_penalty + + posture_penalty + # + ang_vel_penalty + + height_penalty + # + exploration_bonus + # + height_down_penalty + ) + if time.time() - self.start_time >= 1200: + self.start_time = time.time() + print( + # f"progress_reward:{progress_reward:.4f}", + # f"lateral_penalty:{lateral_penalty:.4f}", + # f"action_penalty:{action_penalty:.4f}"s, + f"height_penalty:{height_penalty:.4f}", + f"smoothness_penalty:{smoothness_penalty:.4f},", + f"posture_penalty:{posture_penalty:.4f}", + # f"ang_vel_penalty:{ang_vel_penalty:.4f}", + # f"height_down_penalty:{height_down_penalty:.4f}", + # f"exploration_bonus:{exploration_bonus:.4f}" + ) + + return total + + + + def step(self, action): + + r = self.Player.robot + self.previous_action = action + + 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=25, kd=0.6 + ) + + self.previous_action = action + + 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) + + # Update previous position + self.previous_pos = current_pos.copy() + self.last_action_for_reward = action.copy() + + # Fall detection and penalty + is_fallen = self.Player.world.global_position[2] < 0.3 + + # terminal state: the robot is falling or timeout + terminated = is_fallen or self.step_counter > 800 or self.route_completed + truncated = False + + return self.observe(), reward, terminated, truncated, {} + + +class Train(Train_Base): + def __init__(self, script) -> None: + super().__init__(script) + + def train(self, args): + + # --------------------------------------- Learning parameters + n_envs = 20 # Reduced from 8 to decrease CPU/network pressure during init + if n_envs < 1: + raise ValueError("GYM_CPU_N_ENVS must be >= 1") + n_steps_per_env = 1024 # RolloutBuffer is of size (n_steps_per_env * n_envs) + minibatch_size = 128 # should be a factor of (n_steps_per_env * n_envs) + total_steps = 30000000 + learning_rate = 3e-4 + 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): + def thunk(): + return WalkEnv(self.ip, self.server_p + i_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) # include 1 extra server for testing + + # Wait for servers to start + print(f"Starting {n_envs + 1} rcssservermj servers...") + print("Servers started, creating environments...") + + env = SubprocVecEnv([init_env(i) for i in range(n_envs)]) + eval_env = SubprocVecEnv([init_env(n_envs)]) + + 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=0.005, # Entropy coefficient for exploration + # clip_range=0.13, # PPO clipping parameter + gae_lambda=0.95, # GAE lambda + gamma=0.99 , # Discount factor + # target_kl=0.03, + # n_epochs=5 + ) + + model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env, + eval_freq=n_steps_per_env * 10, save_freq=n_steps_per_env * 10, + 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) + server = Train_Server(self.server_p - 1, self.monitor_p, 1) + 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) + trainer.train({}) + # trainer.test({"model_file": "scripts/gyms/logs/Walk_R0_000/best_model.zip", + # "folder_dir": "scripts/gyms/logs/Walk_R0_000/",}) \ No newline at end of file diff --git a/scripts/gyms/logs/Walk_R0_003/Walk.py b/scripts/gyms/logs/Walk_R0_003/Walk.py new file mode 100644 index 0000000..011e7f0 --- /dev/null +++ b/scripts/gyms/logs/Walk_R0_003/Walk.py @@ -0,0 +1,625 @@ +import os +import numpy as np +import math +import time +from time import sleep +from random import random +from random import uniform + +from stable_baselines3 import PPO +from stable_baselines3.common.vec_env import SubprocVecEnv + +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.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, + 0.0, + 0.0, + 1.4, + 0.0, + -0.4, + 0.0, + -1.4, + 0.0, + 0.4, + 0.0, + -0.4, + 0.0, + 0.0, + 0.8, + -0.4, + 0.0, + 0.4, + 0.0, + 0.0, + -0.8, + 0.4, + 0.0, + ] + ) + 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 + + # Small reset perturbations for robustness training. + self.enable_reset_perturb = True + self.reset_beam_yaw_range_deg = 180 # randomize target direction fully to encourage learning a real walk instead of a fixed gait + self.reset_joint_noise_rad = 0.035 + self.reset_perturb_steps = 5 + self.reset_recover_steps = 8 + + 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.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 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 + + 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.Player.server.receive() + self.Player.world.update() + 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) + + length1 = 2 # randomize target distance + length2 = np.random.uniform(0.6, 1) # randomize target distance + length3 = np.random.uniform(0.6, 1) # randomize target distance + angle2 = np.random.uniform(-30, 30) # randomize initial orientation + angle3 = np.random.uniform(-30, 30) # randomize target direction + + 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.walk_cycle_step = 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.Player.server.receive() + self.Player.world.update() + 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) + # Build target in the robot's current forward direction instead of fixed global +x. + heading_deg = float(r.global_orientation_euler[2]) + forward_offset = MathOps.rotate_2d_vec(np.array([length1, 0.0]), heading_deg, is_rad=False) + point1 = self.initial_position + forward_offset + point2 = point1 + MathOps.rotate_2d_vec(np.array([length2, 0]), angle2, is_rad=False) + point3 = point2 + MathOps.rotate_2d_vec(np.array([length3, 0]), angle3, is_rad=False) + 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]) + + orientation_quat_inv = R.from_quat(self.Player.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(self.Player.robot.gyroscope) + ang_vel_mag = float(np.linalg.norm(ang_vel)) + + is_fallen = height < 0.3 + if is_fallen: + remain = max(0, 800 - self.step_counter) + return -8.0 - 0.01 * remain + + + + # # 目标方向 + # to_target = self.target_position - current_pos + # dist_to_target = float(np.linalg.norm(to_target)) + # if dist_to_target < 0.5: + # return 15.0 + + # forward_dir = to_target / dist_to_target if dist_to_target > 0.1 else np.array([1.0, 0.0]) + # delta_pos = current_pos - previous_pos + # forward_step = float(np.dot(delta_pos, forward_dir)) + # lateral_step = float(np.linalg.norm(delta_pos - forward_dir * forward_step)) + + # 奖励项 + # progress_reward = 2 * forward_step + # lateral_penalty = -0.1 * lateral_step + alive_bonus = 0.3 + + # action_penalty = -0.01 * float(np.linalg.norm(action)) + smoothness_penalty = -0.03 * float(np.linalg.norm(action - self.last_action_for_reward)) + + posture_penalty = -1.0 * (tilt_mag) + ang_vel_penalty = -0.05 * ang_vel_mag + + target_height = self.initial_height + height_error = height - target_height + height_penalty = -2.0 * abs(height_error) # 惩罚高度偏离，系数可调 + + # # 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 + + # lateral_penalty + + # action_penalty + + smoothness_penalty + + posture_penalty + + ang_vel_penalty + + height_penalty + # + exploration_bonus + # + height_down_penalty + ) + if time.time() - self.start_time >= 1200: + self.start_time = time.time() + print( + # f"progress_reward:{progress_reward:.4f}", + # f"lateral_penalty:{lateral_penalty:.4f}", + # f"action_penalty:{action_penalty:.4f}"s, + f"height_penalty:{height_penalty:.4f}", + f"smoothness_penalty:{smoothness_penalty:.4f},", + f"posture_penalty:{posture_penalty:.4f}", + # f"ang_vel_penalty:{ang_vel_penalty:.4f}", + # f"height_down_penalty:{height_down_penalty:.4f}", + # f"exploration_bonus:{exploration_bonus:.4f}" + ) + + return total + + + + def step(self, action): + + r = self.Player.robot + self.previous_action = action + + 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=25, kd=0.6 + ) + + self.previous_action = action + + 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) + + # Update previous position + self.previous_pos = current_pos.copy() + self.last_action_for_reward = action.copy() + + # Fall detection and penalty + is_fallen = self.Player.world.global_position[2] < 0.3 + + # terminal state: the robot is falling or timeout + terminated = is_fallen or self.step_counter > 800 or self.route_completed + truncated = False + + return self.observe(), reward, terminated, truncated, {} + + +class Train(Train_Base): + def __init__(self, script) -> None: + super().__init__(script) + + def train(self, args): + + # --------------------------------------- Learning parameters + n_envs = 20 # Reduced from 8 to decrease CPU/network pressure during init + if n_envs < 1: + raise ValueError("GYM_CPU_N_ENVS must be >= 1") + n_steps_per_env = 1024 # RolloutBuffer is of size (n_steps_per_env * n_envs) + minibatch_size = 128 # should be a factor of (n_steps_per_env * n_envs) + total_steps = 30000000 + learning_rate = 1e-4 + 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): + def thunk(): + return WalkEnv(self.ip, self.server_p + i_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) # include 1 extra server for testing + + # Wait for servers to start + print(f"Starting {n_envs + 1} rcssservermj servers...") + print("Servers started, creating environments...") + + env = SubprocVecEnv([init_env(i) for i in range(n_envs)]) + eval_env = SubprocVecEnv([init_env(n_envs)]) + + 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=0.001, # Entropy coefficient for exploration + # clip_range=0.13, # PPO clipping parameter + gae_lambda=0.95, # GAE lambda + gamma=0.99 , # Discount factor + target_kl=0.03, + # n_epochs=5 + ) + + model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env, + eval_freq=n_steps_per_env * 10, save_freq=n_steps_per_env * 10, + 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) + server = Train_Server(self.server_p - 1, self.monitor_p, 1) + 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) + trainer.train({}) + # trainer.test({"model_file": "scripts/gyms/logs/Walk_R0_000/best_model.zip", + # "folder_dir": "scripts/gyms/logs/Walk_R0_000/",}) \ No newline at end of file diff --git a/scripts/gyms/logs/Walk_R0_004/Walk.py b/scripts/gyms/logs/Walk_R0_004/Walk.py new file mode 100644 index 0000000..f491edb --- /dev/null +++ b/scripts/gyms/logs/Walk_R0_004/Walk.py @@ -0,0 +1,626 @@ +import os +import numpy as np +import math +import time +from time import sleep +from random import random +from random import uniform + +from stable_baselines3 import PPO +from stable_baselines3.common.vec_env import SubprocVecEnv + +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.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, + 0.0, + 0.0, + 1.4, + 0.0, + -0.4, + 0.0, + -1.4, + 0.0, + 0.4, + 0.0, + -0.4, + 0.0, + 0.0, + 0.8, + -0.4, + 0.0, + 0.4, + 0.0, + 0.0, + -0.8, + 0.4, + 0.0, + ] + ) + 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 + + # Small reset perturbations for robustness training. + self.enable_reset_perturb = True + self.reset_beam_yaw_range_deg = 180 # randomize target direction fully to encourage learning a real walk instead of a fixed gait + self.reset_joint_noise_rad = 0.015 + self.reset_perturb_steps = 3 + self.reset_recover_steps = 8 + + 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.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 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 + + 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.Player.server.receive() + self.Player.world.update() + 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) + + length1 = 2 # randomize target distance + length2 = np.random.uniform(0.6, 1) # randomize target distance + length3 = np.random.uniform(0.6, 1) # randomize target distance + angle2 = np.random.uniform(-30, 30) # randomize initial orientation + angle3 = np.random.uniform(-30, 30) # randomize target direction + + 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.walk_cycle_step = 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.Player.server.receive() + self.Player.world.update() + 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) + # Build target in the robot's current forward direction instead of fixed global +x. + heading_deg = float(r.global_orientation_euler[2]) + forward_offset = MathOps.rotate_2d_vec(np.array([length1, 0.0]), heading_deg, is_rad=False) + point1 = self.initial_position + forward_offset + point2 = point1 + MathOps.rotate_2d_vec(np.array([length2, 0]), angle2, is_rad=False) + point3 = point2 + MathOps.rotate_2d_vec(np.array([length3, 0]), angle3, is_rad=False) + 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]) + + orientation_quat_inv = R.from_quat(self.Player.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(self.Player.robot.gyroscope) + ang_vel_mag = float(np.linalg.norm(ang_vel)) + + is_fallen = height < 0.3 + if is_fallen: + # remain = max(0, 800 - self.step_counter) + # return -8.0 - 0.01 * remain + return -1.0 + + + + # # 目标方向 + # to_target = self.target_position - current_pos + # dist_to_target = float(np.linalg.norm(to_target)) + # if dist_to_target < 0.5: + # return 15.0 + + # forward_dir = to_target / dist_to_target if dist_to_target > 0.1 else np.array([1.0, 0.0]) + # delta_pos = current_pos - previous_pos + # forward_step = float(np.dot(delta_pos, forward_dir)) + # lateral_step = float(np.linalg.norm(delta_pos - forward_dir * forward_step)) + + # 奖励项 + # progress_reward = 2 * forward_step + # lateral_penalty = -0.1 * lateral_step + alive_bonus = 2.0 + + # action_penalty = -0.01 * float(np.linalg.norm(action)) + smoothness_penalty = -0.01 * float(np.linalg.norm(action - self.last_action_for_reward)) + + posture_penalty = -0.3 * (tilt_mag) + ang_vel_penalty = -0.02 * ang_vel_mag + + target_height = self.initial_height + height_error = height - target_height + height_penalty = -0.5 * abs(height_error) # 惩罚高度偏离，系数可调 + + # # 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 + + # lateral_penalty + + # action_penalty + + smoothness_penalty + + posture_penalty + + ang_vel_penalty + + height_penalty + # + exploration_bonus + # + height_down_penalty + ) + if time.time() - self.start_time >= 1200: + self.start_time = time.time() + print( + # f"progress_reward:{progress_reward:.4f}", + # f"lateral_penalty:{lateral_penalty:.4f}", + # f"action_penalty:{action_penalty:.4f}"s, + f"height_penalty:{height_penalty:.4f}", + f"smoothness_penalty:{smoothness_penalty:.4f},", + f"posture_penalty:{posture_penalty:.4f}", + # f"ang_vel_penalty:{ang_vel_penalty:.4f}", + # f"height_down_penalty:{height_down_penalty:.4f}", + # f"exploration_bonus:{exploration_bonus:.4f}" + ) + + return total + + + + def step(self, action): + + r = self.Player.robot + self.previous_action = action + + 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=40, kd=1.0 + ) + + self.previous_action = action + + 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) + + # Update previous position + self.previous_pos = current_pos.copy() + self.last_action_for_reward = action.copy() + + # Fall detection and penalty + is_fallen = self.Player.world.global_position[2] < 0.3 + + # terminal state: the robot is falling or timeout + terminated = is_fallen or self.step_counter > 800 or self.route_completed + truncated = False + + return self.observe(), reward, terminated, truncated, {} + + +class Train(Train_Base): + def __init__(self, script) -> None: + super().__init__(script) + + def train(self, args): + + # --------------------------------------- Learning parameters + n_envs = 20 # Reduced from 8 to decrease CPU/network pressure during init + if n_envs < 1: + raise ValueError("GYM_CPU_N_ENVS must be >= 1") + 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 = 3e-4 + 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): + def thunk(): + return WalkEnv(self.ip, self.server_p + i_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) # include 1 extra server for testing + + # Wait for servers to start + print(f"Starting {n_envs + 1} rcssservermj servers...") + print("Servers started, creating environments...") + + env = SubprocVecEnv([init_env(i) for i in range(n_envs)]) + eval_env = SubprocVecEnv([init_env(n_envs)]) + + 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=0.05, # Entropy coefficient for exploration + # clip_range=0.13, # PPO clipping parameter + gae_lambda=0.95, # GAE lambda + gamma=0.95 , # Discount factor + target_kl=0.03, + n_epochs=5 + ) + + model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env, + eval_freq=n_steps_per_env * 10, save_freq=n_steps_per_env * 10, + 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) + server = Train_Server(self.server_p - 1, self.monitor_p, 1) + 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) + trainer.train({}) + # trainer.test({"model_file": "scripts/gyms/logs/Walk_R0_000/best_model.zip", + # "folder_dir": "scripts/gyms/logs/Walk_R0_000/",}) \ No newline at end of file diff --git a/scripts/gyms/logs/Walk_R0_005/Walk.py b/scripts/gyms/logs/Walk_R0_005/Walk.py new file mode 100755 index 0000000..842ac5f --- /dev/null +++ b/scripts/gyms/logs/Walk_R0_005/Walk.py @@ -0,0 +1,660 @@ +import os +import numpy as np +import math +import time +from time import sleep +from random import random +from random import uniform + +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.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, + 0.0, + 0.0, + 1.4, + 0.0, + -0.4, + 0.0, + -1.4, + 0.0, + 0.4, + 0.0, + -0.4, + 0.0, + 0.0, + 0.8, + -0.4, + 0.0, + 0.4, + 0.0, + 0.0, + -0.8, + 0.4, + 0.0, + ] + ) + 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 + + # Small reset perturbations for robustness training. + self.enable_reset_perturb = True + self.reset_beam_yaw_range_deg = 180 # randomize target direction fully to encourage learning a real walk instead of a fixed gait + self.reset_joint_noise_rad = 0.015 + self.reset_perturb_steps = 3 + self.reset_recover_steps = 8 + + 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.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 + + 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) + + length1 = 2 # randomize target distance + length2 = np.random.uniform(0.6, 1) # randomize target distance + length3 = np.random.uniform(0.6, 1) # randomize target distance + angle2 = np.random.uniform(-30, 30) # randomize initial orientation + angle3 = np.random.uniform(-30, 30) # randomize target direction + + 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.walk_cycle_step = 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) + # Build target in the robot's current forward direction instead of fixed global +x. + heading_deg = float(r.global_orientation_euler[2]) + forward_offset = MathOps.rotate_2d_vec(np.array([length1, 0.0]), heading_deg, is_rad=False) + point1 = self.initial_position + forward_offset + point2 = point1 + MathOps.rotate_2d_vec(np.array([length2, 0]), angle2, is_rad=False) + point3 = point2 + MathOps.rotate_2d_vec(np.array([length3, 0]), angle3, is_rad=False) + 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]) + + orientation_quat_inv = R.from_quat(self.Player.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(self.Player.robot.gyroscope) + ang_vel_mag = float(np.linalg.norm(ang_vel)) + + is_fallen = height < 0.3 + if is_fallen: + # remain = max(0, 800 - self.step_counter) + # return -8.0 - 0.01 * remain + return -1.0 + + + + # # 目标方向 + # to_target = self.target_position - current_pos + # dist_to_target = float(np.linalg.norm(to_target)) + # if dist_to_target < 0.5: + # return 15.0 + + # forward_dir = to_target / dist_to_target if dist_to_target > 0.1 else np.array([1.0, 0.0]) + # delta_pos = current_pos - previous_pos + # forward_step = float(np.dot(delta_pos, forward_dir)) + # lateral_step = float(np.linalg.norm(delta_pos - forward_dir * forward_step)) + + # 奖励项 + # progress_reward = 2 * forward_step + # lateral_penalty = -0.1 * lateral_step + alive_bonus = 2.0 + + # action_penalty = -0.01 * float(np.linalg.norm(action)) + smoothness_penalty = -0.01 * float(np.linalg.norm(action - self.last_action_for_reward)) + + posture_penalty = -0.3 * (tilt_mag) + ang_vel_penalty = -0.02 * ang_vel_mag + + target_height = self.initial_height + height_error = height - target_height + height_penalty = -0.5 * abs(height_error) # 惩罚高度偏离，系数可调 + + # # 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 + + # lateral_penalty + + # action_penalty + + smoothness_penalty + + posture_penalty + + ang_vel_penalty + + height_penalty + # + exploration_bonus + # + height_down_penalty + ) + if time.time() - self.start_time >= 1200: + self.start_time = time.time() + print( + # f"progress_reward:{progress_reward:.4f}", + # f"lateral_penalty:{lateral_penalty:.4f}", + # f"action_penalty:{action_penalty:.4f}"s, + f"height_penalty:{height_penalty:.4f}", + f"smoothness_penalty:{smoothness_penalty:.4f},", + f"posture_penalty:{posture_penalty:.4f}", + # f"ang_vel_penalty:{ang_vel_penalty:.4f}", + # f"height_down_penalty:{height_down_penalty:.4f}", + # f"exploration_bonus:{exploration_bonus:.4f}" + ) + + return total + + + + def step(self, action): + + r = self.Player.robot + self.previous_action = action + + 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=40, kd=1.0 + ) + + self.previous_action = action + + 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) + + # Update previous position + self.previous_pos = current_pos.copy() + self.last_action_for_reward = action.copy() + + # Fall detection and penalty + is_fallen = self.Player.world.global_position[2] < 0.3 + + # 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 = 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 = 1e-4 + 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 + + 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) # 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)]) + # 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=0.03, # Entropy coefficient for exploration + clip_range=0.13, # PPO clipping parameter + gae_lambda=0.95, # GAE lambda + gamma=0.95 , # Discount factor + target_kl=0.03, + n_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 * 10, save_freq=n_steps_per_env * 10, + 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) + server = Train_Server(self.server_p - 1, self.monitor_p, 1) + 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) + trainer.train({"model_file": "scripts/gyms/logs/Walk_R0_004/best_model.zip"}) + # trainer.test({"model_file": "scripts/gyms/logs/Walk_R0_004/best_model.zip", + # "folder_dir": "scripts/gyms/logs/Walk_R0_004/",}) \ No newline at end of file diff --git a/scripts/gyms/logs/Walk_R0_006/Walk.py b/scripts/gyms/logs/Walk_R0_006/Walk.py new file mode 100755 index 0000000..7fd8700 --- /dev/null +++ b/scripts/gyms/logs/Walk_R0_006/Walk.py @@ -0,0 +1,679 @@ +import os +import numpy as np +import math +import time +from time import sleep +from random import random +from random import uniform + +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.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, + 0.0, + 0.0, + 1.4, + 0.0, + -0.4, + 0.0, + -1.4, + 0.0, + 0.4, + 0.0, + -0.4, + 0.0, + 0.0, + 0.8, + -0.4, + 0.0, + 0.4, + 0.0, + 0.0, + -0.8, + 0.4, + 0.0, + ] + ) + 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 + + # Small reset perturbations for robustness training. + self.enable_reset_perturb = True + self.reset_beam_yaw_range_deg = 180 # randomize target direction fully to encourage learning a real walk instead of a fixed gait + self.reset_joint_noise_rad = 0.015 + self.reset_perturb_steps = 3 + self.reset_recover_steps = 8 + + 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.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 + + 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) + + length1 = 2 # randomize target distance + length2 = np.random.uniform(0.6, 1) # randomize target distance + length3 = np.random.uniform(0.6, 1) # randomize target distance + angle2 = np.random.uniform(-30, 30) # randomize initial orientation + angle3 = np.random.uniform(-30, 30) # randomize target direction + + 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.walk_cycle_step = 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) + # Build target in the robot's current forward direction instead of fixed global +x. + heading_deg = float(r.global_orientation_euler[2]) + forward_offset = MathOps.rotate_2d_vec(np.array([length1, 0.0]), heading_deg, is_rad=False) + point1 = self.initial_position + forward_offset + point2 = point1 + MathOps.rotate_2d_vec(np.array([length2, 0]), angle2, is_rad=False) + point3 = point2 + MathOps.rotate_2d_vec(np.array([length3, 0]), angle3, is_rad=False) + 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]) + + orientation_quat_inv = R.from_quat(self.Player.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(self.Player.robot.gyroscope) + ang_vel_mag = float(np.linalg.norm(ang_vel)) + + is_fallen = height < 0.3 + if is_fallen: + # remain = max(0, 800 - self.step_counter) + # return -8.0 - 0.01 * remain + return -1.0 + + + + # # 目标方向 + # to_target = self.target_position - current_pos + # dist_to_target = float(np.linalg.norm(to_target)) + # if dist_to_target < 0.5: + # return 15.0 + + # forward_dir = to_target / dist_to_target if dist_to_target > 0.1 else np.array([1.0, 0.0]) + # delta_pos = current_pos - previous_pos + # forward_step = float(np.dot(delta_pos, forward_dir)) + # lateral_step = float(np.linalg.norm(delta_pos - forward_dir * forward_step)) + + # 奖励项 + # progress_reward = 2 * forward_step + # lateral_penalty = -0.1 * lateral_step + alive_bonus = 2.0 + + # action_penalty = -0.01 * float(np.linalg.norm(action)) + smoothness_penalty = -0.01 * float(np.linalg.norm(action - self.last_action_for_reward)) + + posture_penalty = -0.3 * (tilt_mag) + ang_vel_penalty = -0.02 * ang_vel_mag + + target_height = self.initial_height + height_error = height - target_height + height_penalty = -0.5 * abs(height_error) # 惩罚高度偏离，系数可调 + + # # 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 + + # lateral_penalty + + # action_penalty + + smoothness_penalty + + posture_penalty + + ang_vel_penalty + + height_penalty + # + exploration_bonus + # + height_down_penalty + ) + if time.time() - self.start_time >= 600: + self.start_time = time.time() + print( + # f"progress_reward:{progress_reward:.4f}", + # f"lateral_penalty:{lateral_penalty:.4f}", + # f"action_penalty:{action_penalty:.4f}"s, + f"height_penalty:{height_penalty:.4f}", + f"smoothness_penalty:{smoothness_penalty:.4f},", + f"posture_penalty:{posture_penalty:.4f}", + # f"ang_vel_penalty:{ang_vel_penalty:.4f}", + # f"height_down_penalty:{height_down_penalty:.4f}", + # f"exploration_bonus:{exploration_bonus:.4f}" + ) + + return total + + + + def step(self, action): + + r = self.Player.robot + self.previous_action = action + + 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=40, kd=1.0 + ) + + self.previous_action = action + + 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) + + # Update previous position + self.previous_pos = current_pos.copy() + self.last_action_for_reward = action.copy() + + # Fall detection and penalty + is_fallen = self.Player.world.global_position[2] < 0.3 + + # 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", "256")) # 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'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 + + 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)]) + # 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, + 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/Walk_R0_004/best_model.zip") + test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Walk_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 diff --git a/scripts/gyms/logs/Walk_R0_007/Walk.py b/scripts/gyms/logs/Walk_R0_007/Walk.py new file mode 100755 index 0000000..51cf34c --- /dev/null +++ b/scripts/gyms/logs/Walk_R0_007/Walk.py @@ -0,0 +1,679 @@ +import os +import numpy as np +import math +import time +from time import sleep +from random import random +from random import uniform + +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.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, + 0.0, + 0.0, + 1.4, + 0.0, + -0.4, + 0.0, + -1.4, + 0.0, + 0.4, + 0.0, + -0.4, + 0.0, + 0.0, + 0.8, + -0.4, + 0.0, + 0.4, + 0.0, + 0.0, + -0.8, + 0.4, + 0.0, + ] + ) + 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 + + # Small reset perturbations for robustness training. + self.enable_reset_perturb = True + self.reset_beam_yaw_range_deg = 180 # randomize target direction fully to encourage learning a real walk instead of a fixed gait + self.reset_joint_noise_rad = 0.015 + self.reset_perturb_steps = 3 + self.reset_recover_steps = 8 + + 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.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 + + 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) + + length1 = 2 # randomize target distance + length2 = np.random.uniform(0.6, 1) # randomize target distance + length3 = np.random.uniform(0.6, 1) # randomize target distance + angle2 = np.random.uniform(-30, 30) # randomize initial orientation + angle3 = np.random.uniform(-30, 30) # randomize target direction + + 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.walk_cycle_step = 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) + # Build target in the robot's current forward direction instead of fixed global +x. + heading_deg = float(r.global_orientation_euler[2]) + forward_offset = MathOps.rotate_2d_vec(np.array([length1, 0.0]), heading_deg, is_rad=False) + point1 = self.initial_position + forward_offset + point2 = point1 + MathOps.rotate_2d_vec(np.array([length2, 0]), angle2, is_rad=False) + point3 = point2 + MathOps.rotate_2d_vec(np.array([length3, 0]), angle3, is_rad=False) + 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]) + + orientation_quat_inv = R.from_quat(self.Player.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(self.Player.robot.gyroscope) + ang_vel_mag = float(np.linalg.norm(ang_vel)) + + is_fallen = height < 0.3 + if is_fallen: + # remain = max(0, 800 - self.step_counter) + # return -8.0 - 0.01 * remain + return -1.0 + + + + # # 目标方向 + # to_target = self.target_position - current_pos + # dist_to_target = float(np.linalg.norm(to_target)) + # if dist_to_target < 0.5: + # return 15.0 + + # forward_dir = to_target / dist_to_target if dist_to_target > 0.1 else np.array([1.0, 0.0]) + # delta_pos = current_pos - previous_pos + # forward_step = float(np.dot(delta_pos, forward_dir)) + # lateral_step = float(np.linalg.norm(delta_pos - forward_dir * forward_step)) + + # 奖励项 + # progress_reward = 2 * forward_step + # lateral_penalty = -0.1 * lateral_step + alive_bonus = 2.0 + + # action_penalty = -0.01 * float(np.linalg.norm(action)) + smoothness_penalty = -0.01 * float(np.linalg.norm(action - self.last_action_for_reward)) + + posture_penalty = -0.3 * (tilt_mag) + ang_vel_penalty = -0.02 * ang_vel_mag + + target_height = self.initial_height + height_error = height - target_height + height_penalty = -1 * abs(height_error) # 惩罚高度偏离，系数可调 + + # # 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 + + # lateral_penalty + + # action_penalty + + smoothness_penalty + + posture_penalty + + ang_vel_penalty + + height_penalty + # + exploration_bonus + # + height_down_penalty + ) + if time.time() - self.start_time >= 1200: + self.start_time = time.time() + print( + # f"progress_reward:{progress_reward:.4f}", + # f"lateral_penalty:{lateral_penalty:.4f}", + # f"action_penalty:{action_penalty:.4f}"s, + f"height_penalty:{height_penalty:.4f}", + f"smoothness_penalty:{smoothness_penalty:.4f},", + f"posture_penalty:{posture_penalty:.4f}", + # f"ang_vel_penalty:{ang_vel_penalty:.4f}", + # f"height_down_penalty:{height_down_penalty:.4f}", + # f"exploration_bonus:{exploration_bonus:.4f}" + ) + + return total + + + + def step(self, action): + + r = self.Player.robot + self.previous_action = action + + 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=40, kd=1.0 + ) + + self.previous_action = action + + 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) + + # Update previous position + self.previous_pos = current_pos.copy() + self.last_action_for_reward = action.copy() + + # Fall detection and penalty + is_fallen = self.Player.world.global_position[2] < 0.3 + + # 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", "256")) # 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'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 + + 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)]) + # 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=100, + 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/Walk_R0_004/best_model.zip") + test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Walk_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 diff --git a/scripts/gyms/logs/Walk_R0_008/Walk.py b/scripts/gyms/logs/Walk_R0_008/Walk.py new file mode 100755 index 0000000..17a007c --- /dev/null +++ b/scripts/gyms/logs/Walk_R0_008/Walk.py @@ -0,0 +1,704 @@ +import os +import numpy as np +import math +import time +from time import sleep +from random import random +from random import uniform + +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.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, + 0.0, + 0.0, + 1.4, + 0.0, + -0.4, + 0.0, + -1.4, + 0.0, + 0.4, + 0.0, + -0.4, + 0.0, + 0.0, + 0.8, + -0.4, + 0.0, + 0.4, + 0.0, + 0.0, + -0.8, + 0.4, + 0.0, + ] + ) + 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 # randomize target direction fully to encourage learning a real walk instead of a fixed gait + self.reset_joint_noise_rad = 0.015 + self.reset_perturb_steps = 3 + self.reset_recover_steps = 8 + + 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.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 + + 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) + + length1 = 2 # randomize target distance + length2 = np.random.uniform(0.6, 1) # randomize target distance + length3 = np.random.uniform(0.6, 1) # randomize target distance + angle2 = np.random.uniform(-30, 30) # randomize initial orientation + angle3 = np.random.uniform(-30, 30) # randomize target direction + + 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.walk_cycle_step = 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) + # Build target in the robot's current forward direction instead of fixed global +x. + heading_deg = float(r.global_orientation_euler[2]) + forward_offset = MathOps.rotate_2d_vec(np.array([length1, 0.0]), heading_deg, is_rad=False) + point1 = self.initial_position + forward_offset + point2 = point1 + MathOps.rotate_2d_vec(np.array([length2, 0]), angle2, is_rad=False) + point3 = point2 + MathOps.rotate_2d_vec(np.array([length3, 0]), angle3, is_rad=False) + 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 + + 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) + ang_vel_mag = float(np.linalg.norm(ang_vel)) + + is_fallen = height < 0.3 + if is_fallen: + # remain = max(0, 800 - self.step_counter) + # return -8.0 - 0.01 * remain + return -1.0 + + + + # # 目标方向 + # to_target = self.target_position - current_pos + # dist_to_target = float(np.linalg.norm(to_target)) + # if dist_to_target < 0.5: + # return 15.0 + + # forward_dir = to_target / dist_to_target if dist_to_target > 0.1 else np.array([1.0, 0.0]) + # delta_pos = current_pos - previous_pos + # forward_step = float(np.dot(delta_pos, forward_dir)) + # lateral_step = float(np.linalg.norm(delta_pos - forward_dir * forward_step)) + + # 奖励项 + # progress_reward = 2 * forward_step + # lateral_penalty = -0.1 * lateral_step + alive_bonus = 2.0 + + # action_penalty = -0.01 * float(np.linalg.norm(action)) + smoothness_penalty = -0.01 * float(np.linalg.norm(action - self.last_action_for_reward)) + + posture_penalty = -0.3 * (tilt_mag) + ang_vel_penalty = -0.02 * ang_vel_mag + + # Use simulator joint readings in training frame to shape lateral stance. + 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]) + + hip_spread = left_hip_roll - right_hip_roll + ankle_spread = left_ankle_roll - right_ankle_roll + stance_metric = 0.6 * abs(hip_spread) + 0.4 * abs(ankle_spread) + + # Penalize narrow stance (feet too close) and scissoring (cross-leg pattern). + stance_collapse_penalty = -4.0 * max(0.0, self.min_stance_rad - stance_metric) + cross_leg_penalty = -1.2 * max(0.0, -(hip_spread * ankle_spread)) + + target_height = self.initial_height + height_error = height - target_height + height_penalty = -0.5 * abs(height_error) # 惩罚高度偏离，系数可调 + + # # 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 + + # lateral_penalty + + # action_penalty + + smoothness_penalty + + posture_penalty + + ang_vel_penalty + + height_penalty + + stance_collapse_penalty + + cross_leg_penalty + # + exploration_bonus + # + height_down_penalty + ) + if time.time() - self.start_time >= 600: + self.start_time = time.time() + print( + # f"progress_reward:{progress_reward:.4f}", + # f"lateral_penalty:{lateral_penalty:.4f}", + # f"action_penalty:{action_penalty:.4f}"s, + f"height_penalty:{height_penalty:.4f}", + f"smoothness_penalty:{smoothness_penalty:.4f},", + f"posture_penalty:{posture_penalty:.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"height_down_penalty:{height_down_penalty:.4f}", + # f"exploration_bonus:{exploration_bonus:.4f}" + ) + + return total + + + + def step(self, action): + + r = self.Player.robot + self.previous_action = action + + 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=40, kd=1.0 + ) + + self.previous_action = action + + 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) + + # Update previous position + self.previous_pos = current_pos.copy() + self.last_action_for_reward = action.copy() + + # Fall detection and penalty + is_fallen = self.Player.world.global_position[2] < 0.3 + + # 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", "256")) # 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'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 + + 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)]) + # 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=100, + 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/Walk_R0_004/best_model.zip") + test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Walk_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 diff --git a/scripts/gyms/logs/Walk_R0_009/Walk.py b/scripts/gyms/logs/Walk_R0_009/Walk.py new file mode 100755 index 0000000..680e9e1 --- /dev/null +++ b/scripts/gyms/logs/Walk_R0_009/Walk.py @@ -0,0 +1,705 @@ +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.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, + 0.0, + 0.0, + 1.4, + 0.0, + -0.4, + 0.0, + -1.4, + 0.0, + 0.4, + 0.0, + -0.4, + 0.0, + 0.0, + 0.8, + -0.4, + 0.0, + 0.4, + 0.0, + 0.0, + -0.8, + 0.4, + 0.0, + ] + ) + 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 # randomize target direction fully to encourage learning a real walk instead of a fixed gait + self.reset_joint_noise_rad = 0.015 + self.reset_perturb_steps = 3 + self.reset_recover_steps = 8 + + 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.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 + + 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) + + length1 = 2 # randomize target distance + length2 = np.random.uniform(0.6, 1) # randomize target distance + length3 = np.random.uniform(0.6, 1) # randomize target distance + angle2 = np.random.uniform(-30, 30) # randomize initial orientation + angle3 = np.random.uniform(-30, 30) # randomize target direction + + 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.walk_cycle_step = 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) + # Build target in the robot's current forward direction instead of fixed global +x. + heading_deg = float(r.global_orientation_euler[2]) + forward_offset = MathOps.rotate_2d_vec(np.array([length1, 0.0]), heading_deg, is_rad=False) + point1 = self.initial_position + forward_offset + point2 = point1 + MathOps.rotate_2d_vec(np.array([length2, 0]), angle2, is_rad=False) + point3 = point2 + MathOps.rotate_2d_vec(np.array([length3, 0]), angle3, is_rad=False) + 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 + + 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) + ang_vel_mag = float(np.linalg.norm(ang_vel)) + + is_fallen = height < 0.3 + if is_fallen: + # remain = max(0, 800 - self.step_counter) + # return -8.0 - 0.01 * remain + return -1.0 + + + + # # 目标方向 + # to_target = self.target_position - current_pos + # dist_to_target = float(np.linalg.norm(to_target)) + # if dist_to_target < 0.5: + # return 15.0 + + # forward_dir = to_target / dist_to_target if dist_to_target > 0.1 else np.array([1.0, 0.0]) + # delta_pos = current_pos - previous_pos + # forward_step = float(np.dot(delta_pos, forward_dir)) + # lateral_step = float(np.linalg.norm(delta_pos - forward_dir * forward_step)) + + # 奖励项 + # progress_reward = 2 * forward_step + # lateral_penalty = -0.1 * lateral_step + alive_bonus = 2.0 + + # action_penalty = -0.01 * float(np.linalg.norm(action)) + smoothness_penalty = -0.01 * float(np.linalg.norm(action - self.last_action_for_reward)) + + posture_penalty = -0.3 * (tilt_mag) + ang_vel_penalty = -0.02 * ang_vel_mag + + # Use simulator joint readings in training frame to shape lateral stance. + 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]) + + hip_spread = left_hip_roll - right_hip_roll + ankle_spread = left_ankle_roll - right_ankle_roll + stance_metric = 0.6 * abs(hip_spread) + 0.4 * abs(ankle_spread) + + # Penalize narrow stance (feet too close) and scissoring (cross-leg pattern). + stance_collapse_penalty = -4.0 * max(0.0, self.min_stance_rad - stance_metric) + cross_leg_penalty = -1.2 * max(0.0, -(hip_spread * ankle_spread)) + + target_height = self.initial_height + height_error = height - target_height + height_penalty = -0.5 * abs(height_error) # 惩罚高度偏离，系数可调 + + # # 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 + + # lateral_penalty + + # action_penalty + + smoothness_penalty + + posture_penalty + + ang_vel_penalty + + height_penalty + + stance_collapse_penalty + + cross_leg_penalty + # + exploration_bonus + # + height_down_penalty + ) + if time.time() - self.start_time >= 600: + self.start_time = time.time() + print( + # f"progress_reward:{progress_reward:.4f}", + # f"lateral_penalty:{lateral_penalty:.4f}", + # f"action_penalty:{action_penalty:.4f}"s, + f"height_penalty:{height_penalty:.4f}", + f"smoothness_penalty:{smoothness_penalty:.4f},", + f"posture_penalty:{posture_penalty:.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"height_down_penalty:{height_down_penalty:.4f}", + # f"exploration_bonus:{exploration_bonus:.4f}" + ) + + return total + + + + def step(self, action): + + r = self.Player.robot + self.previous_action = action + + 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=40, kd=1.0 + ) + + self.previous_action = action + + 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) + + # Update previous position + self.previous_pos = current_pos.copy() + self.last_action_for_reward = action.copy() + + # Fall detection and penalty + is_fallen = self.Player.world.global_position[2] < 0.3 + + # 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", "256")) # 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'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 + + 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)]) + # 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=100, + 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/Walk_R0_004/best_model.zip") + test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Walk_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 diff --git a/scripts/gyms/logs/Walk_R0_010/Walk.py b/scripts/gyms/logs/Walk_R0_010/Walk.py new file mode 100755 index 0000000..30c0d8e --- /dev/null +++ b/scripts/gyms/logs/Walk_R0_010/Walk.py @@ -0,0 +1,705 @@ +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.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, + 0.0, + 0.0, + 1.4, + 0.0, + -0.4, + 0.0, + -1.4, + 0.0, + 0.4, + 0.0, + -0.4, + 0.0, + 0.0, + 0.8, + -0.4, + 0.0, + 0.4, + 0.0, + 0.0, + -0.8, + 0.4, + 0.0, + ] + ) + 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 # randomize target direction fully to encourage learning a real walk instead of a fixed gait + self.reset_joint_noise_rad = 0.015 + self.reset_perturb_steps = 3 + self.reset_recover_steps = 8 + + 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.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 + + 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) + + length1 = 2 # randomize target distance + length2 = np.random.uniform(0.6, 1) # randomize target distance + length3 = np.random.uniform(0.6, 1) # randomize target distance + angle2 = np.random.uniform(-30, 30) # randomize initial orientation + angle3 = np.random.uniform(-30, 30) # randomize target direction + + 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.walk_cycle_step = 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) + # Build target in the robot's current forward direction instead of fixed global +x. + heading_deg = float(r.global_orientation_euler[2]) + forward_offset = MathOps.rotate_2d_vec(np.array([length1, 0.0]), heading_deg, is_rad=False) + point1 = self.initial_position + forward_offset + point2 = point1 + MathOps.rotate_2d_vec(np.array([length2, 0]), angle2, is_rad=False) + point3 = point2 + MathOps.rotate_2d_vec(np.array([length3, 0]), angle3, is_rad=False) + 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 + + 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) + ang_vel_mag = float(np.linalg.norm(ang_vel)) + + is_fallen = height < 0.55 + if is_fallen: + # remain = max(0, 800 - self.step_counter) + # return -8.0 - 0.01 * remain + return -1.0 + + + + # # 目标方向 + # to_target = self.target_position - current_pos + # dist_to_target = float(np.linalg.norm(to_target)) + # if dist_to_target < 0.5: + # return 15.0 + + # forward_dir = to_target / dist_to_target if dist_to_target > 0.1 else np.array([1.0, 0.0]) + # delta_pos = current_pos - previous_pos + # forward_step = float(np.dot(delta_pos, forward_dir)) + # lateral_step = float(np.linalg.norm(delta_pos - forward_dir * forward_step)) + + # 奖励项 + # progress_reward = 2 * forward_step + # lateral_penalty = -0.1 * lateral_step + alive_bonus = 2.0 + + # action_penalty = -0.01 * float(np.linalg.norm(action)) + smoothness_penalty = -0.01 * float(np.linalg.norm(action - self.last_action_for_reward)) + + posture_penalty = -0.3 * (tilt_mag) + ang_vel_penalty = -0.02 * ang_vel_mag + + # Use simulator joint readings in training frame to shape lateral stance. + 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]) + + hip_spread = left_hip_roll - right_hip_roll + ankle_spread = left_ankle_roll - right_ankle_roll + stance_metric = 0.6 * abs(hip_spread) + 0.4 * abs(ankle_spread) + + # Penalize narrow stance (feet too close) and scissoring (cross-leg pattern). + stance_collapse_penalty = -4.0 * max(0.0, self.min_stance_rad - stance_metric) + cross_leg_penalty = -1.2 * max(0.0, -(hip_spread * ankle_spread)) + + target_height = self.initial_height + height_error = height - target_height + height_penalty = -0.5 * abs(height_error) # 惩罚高度偏离，系数可调 + + # # 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 + + # lateral_penalty + + # action_penalty + + smoothness_penalty + + posture_penalty + + ang_vel_penalty + + height_penalty + + stance_collapse_penalty + + cross_leg_penalty + # + exploration_bonus + # + height_down_penalty + ) + if time.time() - self.start_time >= 600: + self.start_time = time.time() + print( + # f"progress_reward:{progress_reward:.4f}", + # f"lateral_penalty:{lateral_penalty:.4f}", + # f"action_penalty:{action_penalty:.4f}"s, + f"height_penalty:{height_penalty:.4f}", + f"smoothness_penalty:{smoothness_penalty:.4f},", + f"posture_penalty:{posture_penalty:.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"height_down_penalty:{height_down_penalty:.4f}", + # f"exploration_bonus:{exploration_bonus:.4f}" + ) + + return total + + + + def step(self, action): + + r = self.Player.robot + self.previous_action = action + + 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=40, kd=1.0 + ) + + self.previous_action = action + + 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) + + # Update previous position + self.previous_pos = current_pos.copy() + 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", "256")) # 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'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 + + 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)]) + # 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=100, + 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/Walk_R0_004/best_model.zip") + test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Walk_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 diff --git a/scripts/gyms/logs/stand_stable_0.1/Walk.py b/scripts/gyms/logs/stand_stable_0.1/Walk.py new file mode 100644 index 0000000..f491edb --- /dev/null +++ b/scripts/gyms/logs/stand_stable_0.1/Walk.py @@ -0,0 +1,626 @@ +import os +import numpy as np +import math +import time +from time import sleep +from random import random +from random import uniform + +from stable_baselines3 import PPO +from stable_baselines3.common.vec_env import SubprocVecEnv + +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.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, + 0.0, + 0.0, + 1.4, + 0.0, + -0.4, + 0.0, + -1.4, + 0.0, + 0.4, + 0.0, + -0.4, + 0.0, + 0.0, + 0.8, + -0.4, + 0.0, + 0.4, + 0.0, + 0.0, + -0.8, + 0.4, + 0.0, + ] + ) + 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 + + # Small reset perturbations for robustness training. + self.enable_reset_perturb = True + self.reset_beam_yaw_range_deg = 180 # randomize target direction fully to encourage learning a real walk instead of a fixed gait + self.reset_joint_noise_rad = 0.015 + self.reset_perturb_steps = 3 + self.reset_recover_steps = 8 + + 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.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 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 + + 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.Player.server.receive() + self.Player.world.update() + 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) + + length1 = 2 # randomize target distance + length2 = np.random.uniform(0.6, 1) # randomize target distance + length3 = np.random.uniform(0.6, 1) # randomize target distance + angle2 = np.random.uniform(-30, 30) # randomize initial orientation + angle3 = np.random.uniform(-30, 30) # randomize target direction + + 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.walk_cycle_step = 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.Player.server.receive() + self.Player.world.update() + 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) + # Build target in the robot's current forward direction instead of fixed global +x. + heading_deg = float(r.global_orientation_euler[2]) + forward_offset = MathOps.rotate_2d_vec(np.array([length1, 0.0]), heading_deg, is_rad=False) + point1 = self.initial_position + forward_offset + point2 = point1 + MathOps.rotate_2d_vec(np.array([length2, 0]), angle2, is_rad=False) + point3 = point2 + MathOps.rotate_2d_vec(np.array([length3, 0]), angle3, is_rad=False) + 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]) + + orientation_quat_inv = R.from_quat(self.Player.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(self.Player.robot.gyroscope) + ang_vel_mag = float(np.linalg.norm(ang_vel)) + + is_fallen = height < 0.3 + if is_fallen: + # remain = max(0, 800 - self.step_counter) + # return -8.0 - 0.01 * remain + return -1.0 + + + + # # 目标方向 + # to_target = self.target_position - current_pos + # dist_to_target = float(np.linalg.norm(to_target)) + # if dist_to_target < 0.5: + # return 15.0 + + # forward_dir = to_target / dist_to_target if dist_to_target > 0.1 else np.array([1.0, 0.0]) + # delta_pos = current_pos - previous_pos + # forward_step = float(np.dot(delta_pos, forward_dir)) + # lateral_step = float(np.linalg.norm(delta_pos - forward_dir * forward_step)) + + # 奖励项 + # progress_reward = 2 * forward_step + # lateral_penalty = -0.1 * lateral_step + alive_bonus = 2.0 + + # action_penalty = -0.01 * float(np.linalg.norm(action)) + smoothness_penalty = -0.01 * float(np.linalg.norm(action - self.last_action_for_reward)) + + posture_penalty = -0.3 * (tilt_mag) + ang_vel_penalty = -0.02 * ang_vel_mag + + target_height = self.initial_height + height_error = height - target_height + height_penalty = -0.5 * abs(height_error) # 惩罚高度偏离，系数可调 + + # # 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 + + # lateral_penalty + + # action_penalty + + smoothness_penalty + + posture_penalty + + ang_vel_penalty + + height_penalty + # + exploration_bonus + # + height_down_penalty + ) + if time.time() - self.start_time >= 1200: + self.start_time = time.time() + print( + # f"progress_reward:{progress_reward:.4f}", + # f"lateral_penalty:{lateral_penalty:.4f}", + # f"action_penalty:{action_penalty:.4f}"s, + f"height_penalty:{height_penalty:.4f}", + f"smoothness_penalty:{smoothness_penalty:.4f},", + f"posture_penalty:{posture_penalty:.4f}", + # f"ang_vel_penalty:{ang_vel_penalty:.4f}", + # f"height_down_penalty:{height_down_penalty:.4f}", + # f"exploration_bonus:{exploration_bonus:.4f}" + ) + + return total + + + + def step(self, action): + + r = self.Player.robot + self.previous_action = action + + 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=40, kd=1.0 + ) + + self.previous_action = action + + 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) + + # Update previous position + self.previous_pos = current_pos.copy() + self.last_action_for_reward = action.copy() + + # Fall detection and penalty + is_fallen = self.Player.world.global_position[2] < 0.3 + + # terminal state: the robot is falling or timeout + terminated = is_fallen or self.step_counter > 800 or self.route_completed + truncated = False + + return self.observe(), reward, terminated, truncated, {} + + +class Train(Train_Base): + def __init__(self, script) -> None: + super().__init__(script) + + def train(self, args): + + # --------------------------------------- Learning parameters + n_envs = 20 # Reduced from 8 to decrease CPU/network pressure during init + if n_envs < 1: + raise ValueError("GYM_CPU_N_ENVS must be >= 1") + 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 = 3e-4 + 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): + def thunk(): + return WalkEnv(self.ip, self.server_p + i_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) # include 1 extra server for testing + + # Wait for servers to start + print(f"Starting {n_envs + 1} rcssservermj servers...") + print("Servers started, creating environments...") + + env = SubprocVecEnv([init_env(i) for i in range(n_envs)]) + eval_env = SubprocVecEnv([init_env(n_envs)]) + + 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=0.05, # Entropy coefficient for exploration + # clip_range=0.13, # PPO clipping parameter + gae_lambda=0.95, # GAE lambda + gamma=0.95 , # Discount factor + target_kl=0.03, + n_epochs=5 + ) + + model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env, + eval_freq=n_steps_per_env * 10, save_freq=n_steps_per_env * 10, + 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) + server = Train_Server(self.server_p - 1, self.monitor_p, 1) + 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) + trainer.train({}) + # trainer.test({"model_file": "scripts/gyms/logs/Walk_R0_000/best_model.zip", + # "folder_dir": "scripts/gyms/logs/Walk_R0_000/",}) \ No newline at end of file diff --git a/scripts/gyms/logs/stand_stable_final/Walk.py b/scripts/gyms/logs/stand_stable_final/Walk.py new file mode 100755 index 0000000..30c0d8e --- /dev/null +++ b/scripts/gyms/logs/stand_stable_final/Walk.py @@ -0,0 +1,705 @@ +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.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, + 0.0, + 0.0, + 1.4, + 0.0, + -0.4, + 0.0, + -1.4, + 0.0, + 0.4, + 0.0, + -0.4, + 0.0, + 0.0, + 0.8, + -0.4, + 0.0, + 0.4, + 0.0, + 0.0, + -0.8, + 0.4, + 0.0, + ] + ) + 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 # randomize target direction fully to encourage learning a real walk instead of a fixed gait + self.reset_joint_noise_rad = 0.015 + self.reset_perturb_steps = 3 + self.reset_recover_steps = 8 + + 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.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 + + 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) + + length1 = 2 # randomize target distance + length2 = np.random.uniform(0.6, 1) # randomize target distance + length3 = np.random.uniform(0.6, 1) # randomize target distance + angle2 = np.random.uniform(-30, 30) # randomize initial orientation + angle3 = np.random.uniform(-30, 30) # randomize target direction + + 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.walk_cycle_step = 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) + # Build target in the robot's current forward direction instead of fixed global +x. + heading_deg = float(r.global_orientation_euler[2]) + forward_offset = MathOps.rotate_2d_vec(np.array([length1, 0.0]), heading_deg, is_rad=False) + point1 = self.initial_position + forward_offset + point2 = point1 + MathOps.rotate_2d_vec(np.array([length2, 0]), angle2, is_rad=False) + point3 = point2 + MathOps.rotate_2d_vec(np.array([length3, 0]), angle3, is_rad=False) + 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 + + 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) + ang_vel_mag = float(np.linalg.norm(ang_vel)) + + is_fallen = height < 0.55 + if is_fallen: + # remain = max(0, 800 - self.step_counter) + # return -8.0 - 0.01 * remain + return -1.0 + + + + # # 目标方向 + # to_target = self.target_position - current_pos + # dist_to_target = float(np.linalg.norm(to_target)) + # if dist_to_target < 0.5: + # return 15.0 + + # forward_dir = to_target / dist_to_target if dist_to_target > 0.1 else np.array([1.0, 0.0]) + # delta_pos = current_pos - previous_pos + # forward_step = float(np.dot(delta_pos, forward_dir)) + # lateral_step = float(np.linalg.norm(delta_pos - forward_dir * forward_step)) + + # 奖励项 + # progress_reward = 2 * forward_step + # lateral_penalty = -0.1 * lateral_step + alive_bonus = 2.0 + + # action_penalty = -0.01 * float(np.linalg.norm(action)) + smoothness_penalty = -0.01 * float(np.linalg.norm(action - self.last_action_for_reward)) + + posture_penalty = -0.3 * (tilt_mag) + ang_vel_penalty = -0.02 * ang_vel_mag + + # Use simulator joint readings in training frame to shape lateral stance. + 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]) + + hip_spread = left_hip_roll - right_hip_roll + ankle_spread = left_ankle_roll - right_ankle_roll + stance_metric = 0.6 * abs(hip_spread) + 0.4 * abs(ankle_spread) + + # Penalize narrow stance (feet too close) and scissoring (cross-leg pattern). + stance_collapse_penalty = -4.0 * max(0.0, self.min_stance_rad - stance_metric) + cross_leg_penalty = -1.2 * max(0.0, -(hip_spread * ankle_spread)) + + target_height = self.initial_height + height_error = height - target_height + height_penalty = -0.5 * abs(height_error) # 惩罚高度偏离，系数可调 + + # # 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 + + # lateral_penalty + + # action_penalty + + smoothness_penalty + + posture_penalty + + ang_vel_penalty + + height_penalty + + stance_collapse_penalty + + cross_leg_penalty + # + exploration_bonus + # + height_down_penalty + ) + if time.time() - self.start_time >= 600: + self.start_time = time.time() + print( + # f"progress_reward:{progress_reward:.4f}", + # f"lateral_penalty:{lateral_penalty:.4f}", + # f"action_penalty:{action_penalty:.4f}"s, + f"height_penalty:{height_penalty:.4f}", + f"smoothness_penalty:{smoothness_penalty:.4f},", + f"posture_penalty:{posture_penalty:.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"height_down_penalty:{height_down_penalty:.4f}", + # f"exploration_bonus:{exploration_bonus:.4f}" + ) + + return total + + + + def step(self, action): + + r = self.Player.robot + self.previous_action = action + + 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=40, kd=1.0 + ) + + self.previous_action = action + + 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) + + # Update previous position + self.previous_pos = current_pos.copy() + 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", "256")) # 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'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 + + 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)]) + # 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=100, + 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/Walk_R0_004/best_model.zip") + test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Walk_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