101 lines
3.3 KiB
Python
101 lines
3.3 KiB
Python
import sys
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import os
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import argparse
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# 确保能找到项目根目录下的模块
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sys.path.append(os.path.dirname(os.path.abspath(__file__)))
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from isaaclab.app import AppLauncher
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# 1. 配置启动参数
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parser = argparse.ArgumentParser(description="Train T1 robot to Get-Up with RL-Games.")
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parser.add_argument("--num_envs", type=int, default=8192, help="起身任务建议并行 4096 即可")
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parser.add_argument("--task", type=str, default="Isaac-T1-GetUp-v0", help="任务 ID")
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parser.add_argument("--seed", type=int, default=42, help="随机种子")
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AppLauncher.add_app_launcher_args(parser)
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args_cli = parser.parse_args()
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# 2. 启动仿真器（必须在导入其他 isaaclab 模块前）
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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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import yaml
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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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from rl_games.common import env_configurations, vecenv
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# 导入你刚刚修改好的配置类
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# 假设你的文件名是 t1_getup_cfg.py，类名是 T1EnvCfg
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from config.t1_env_cfg import T1EnvCfg
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# 3. 注册环境
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gym.register(
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id="Isaac-T1-GetUp-v0",
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entry_point="isaaclab.envs:ManagerBasedRLEnv",
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kwargs={
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"cfg": T1EnvCfg(), # 这里会加载你设置的随机旋转、时间惩罚等
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},
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)
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def main():
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# --- 新增：处理 Retrain 参数 ---
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# 你可以手动指定路径，或者在 argparse 里增加一个 --checkpoint 参数
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checkpoint_path = os.path.join(os.path.dirname(__file__), "logs/T1_GetUp/nn/T1_GetUp.pth")
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# 检查模型文件是否存在
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should_retrain = os.path.exists(checkpoint_path)
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env = gym.make("Isaac-T1-GetUp-v0", num_envs=args_cli.num_envs)
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# 注意：rl_device 必须设置为 args_cli.device (通常是 'cuda:0')
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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=1.0
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)
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vecenv.register('as_is', lambda config_name, num_actors, **kwargs: wrapped_env)
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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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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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# 设置日志和实验名称
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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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rl_config['params']['config']['name'] = "T1_GetUp"
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# --- 关键修改：注入模型路径 ---
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if should_retrain:
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print(f"[INFO]: 检测到预训练模型，正在从 {checkpoint_path} 恢复训练...")
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# rl_games 会读取 config 中的 load_path 进行续训
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rl_config['params']['config']['load_path'] = checkpoint_path
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else:
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print("[INFO]: 未找到预训练模型，将从零开始训练。")
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# 7. 运行训练
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runner = Runner()
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runner.load(rl_config)
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runner.run({
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"train": True,
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"play": False,
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# 如果你想强制从某个 checkpoint 开始，也可以在这里传参
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"checkpoint": checkpoint_path if should_retrain else None,
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"vec_env": wrapped_env
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})
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simulation_app.close()
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if __name__ == "__main__":
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main() |