83 lines
2.3 KiB
Python
83 lines
2.3 KiB
Python
import sys
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import os
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# 关键：确保当前目录在 sys.path 中，这样才能直接 from config 导入
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sys.path.append(os.path.dirname(os.path.abspath(__file__)))
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import argparse
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from isaaclab.app import AppLauncher
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# 添加启动参数
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parser = argparse.ArgumentParser(description="Train T1 robot with rl_games.")
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parser.add_argument("--num_envs", type=int, default=16384, help="Number of envs to run.")
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AppLauncher.add_app_launcher_args(parser)
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args_cli = parser.parse_args()
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# 启动仿真器
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app_launcher = AppLauncher(args_cli)
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simulation_app = app_launcher.app
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import torch
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import gymnasium as gym
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from isaaclab_rl.rl_games import RlGamesVecEnvWrapper
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from rl_games.torch_runner import Runner
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import yaml
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from config.t1_env_cfg import T1EnvCfg
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from rl_games.common import env_configurations, vecenv
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gym.register(
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id="Isaac-T1-Walking-v0",
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entry_point="isaaclab.envs:ManagerBasedRLEnv", # Isaac Lab 统一的强化学习环境入口
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kwargs={
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"cfg": T1EnvCfg(),
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},
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)
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def main():
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# 1. 创建环境 (保持不变)
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env = gym.make("Isaac-T1-Walking-v0", num_envs=args_cli.num_envs)
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# 2. 包装环境 (保持不变)
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wrapped_env = RlGamesVecEnvWrapper(
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env,
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rl_device=args_cli.device,
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clip_obs=5.0,
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clip_actions=100.0
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)
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vecenv.register('as_is', lambda config_name, num_actors, **kwargs: wrapped_env)
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# 注册环境配置
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env_configurations.register('rlgym', {
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'vecenv_type': 'as_is',
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'env_creator': lambda **kwargs: wrapped_env
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})
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# 3. 加载 PPO 配置 (保持不变)
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config_path = os.path.join(os.path.dirname(__file__), "config", "ppo_cfg.yaml")
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with open(config_path, "r") as f:
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rl_config = yaml.safe_load(f)
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# 设置日志路径
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rl_game_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "."))
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log_dir = os.path.join(rl_game_dir, "logs")
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rl_config['params']['config']['train_dir'] = log_dir
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# 4. 启动训练
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runner = Runner()
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# 此时 rl_config 只有文本和数字，没有复杂对象，deepcopy 会成功
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runner.load(rl_config)
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# 在 run 时传入对象是安全的
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runner.run({
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"train": True,
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"play": False,
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"vec_env": wrapped_env
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})
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simulation_app.close()
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# PYTHONPATH=. python rl_game/your_file_name/train.py
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if __name__ == "__main__":
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main() |