delete play.py commit it in run

rl_game/demo/play.py

@@ -1,57 +0,0 @@
-import sys
-import os
-# 确保当前目录 (demo) 在 sys.path 中
-sys.path.append(os.path.dirname(os.path.abspath(__file__)))
-import argparse
-from isaaclab.app import AppLauncher
-
-# 启动参数，默认关闭 headless 模式以便观察
-parser = argparse.ArgumentParser(description="Play trained T1 policy.")
-parser.add_argument("--checkpoint", type=str, help="Path to the trained model checkpoint.")
-AppLauncher.add_app_launcher_args(parser)
-args_cli = parser.parse_args()
-
-app_launcher = AppLauncher(args_cli)
-simulation_app = app_launcher.app
-
-import torch
-import gymnasium as gym
-from isaaclab_rl.rl_games import RlGamesVecEnvWrapper
-from rl_games.torch_runner import Runner
-import yaml
-import os
-
-from rl_game.demo.config.t1_env_cfg import T1EnvCfg
-
-gym.register(
-    id="Isaac-T1-Walking-v0",
-    entry_point="isaaclab.envs:ManagerBasedRLEnv", # Isaac Lab 统一的强化学习环境入口
-    kwargs={
-        "cfg": T1EnvCfg(),
-    },
-)
-
-
-def main():
-    # 1. 创建环境 (只给 1 个机器人看演示)
-    env = gym.make("Isaac-T1-Walking-v0", num_envs=1, render_mode="rgb_array")
-    wrapped_env = RlGamesVecEnvWrapper(env)
-
-    # 2. 加载配置并推理
-    config_path = os.path.join(os.path.dirname(__file__), "config", "ppo_cfg.yaml")
-    with open(config_path, "r") as f:
-        rl_config = yaml.safe_load(f)
-
-    # 注入 checkpoint 路径
-    rl_config['params']['config']['checkpoint'] = args_cli.checkpoint
-
-    runner = Runner()
-    runner.load(rl_config)
-    # 运行推理模式
-    runner.run({"vecenv": wrapped_env, "train": False})
-
-    simulation_app.close()
-
-
-if __name__ == "__main__":
-    main()