| from typing import Callable |
|
|
| import gymnasium as gym |
| import torch |
| import torch.nn as nn |
|
|
|
|
| def evaluate( |
| model_path: str, |
| make_env: Callable, |
| env_id: str, |
| eval_episodes: int, |
| run_name: str, |
| Model: nn.Module, |
| device: torch.device = torch.device("cpu"), |
| capture_video: bool = True, |
| exploration_noise: float = 0.1, |
| ): |
| envs = gym.vector.SyncVectorEnv([make_env(env_id, 0, 0, capture_video, run_name)]) |
| actor = Model[0](envs).to(device) |
| qf1 = Model[1](envs).to(device) |
| qf2 = Model[1](envs).to(device) |
| actor_params, qf1_params, qf2_params = torch.load(model_path, map_location=device) |
| actor.load_state_dict(actor_params) |
| actor.eval() |
| qf1.load_state_dict(qf1_params) |
| qf2.load_state_dict(qf2_params) |
| qf1.eval() |
| qf2.eval() |
| |
|
|
| obs, _ = envs.reset() |
| episodic_returns = [] |
| while len(episodic_returns) < eval_episodes: |
| with torch.no_grad(): |
| actions = actor(torch.Tensor(obs).to(device)) |
| actions += torch.normal(0, actor.action_scale * exploration_noise) |
| actions = actions.cpu().numpy().clip(envs.single_action_space.low, envs.single_action_space.high) |
|
|
| next_obs, _, _, _, infos = envs.step(actions) |
| if "final_info" in infos: |
| for info in infos["final_info"]: |
| if "episode" not in info: |
| continue |
| print(f"eval_episode={len(episodic_returns)}, episodic_return={info['episode']['r']}") |
| episodic_returns += [info["episode"]["r"]] |
| obs = next_obs |
|
|
| return episodic_returns |
|
|
|
|
| if __name__ == "__main__": |
| from huggingface_hub import hf_hub_download |
|
|
| from cleanrl.td3_continuous_action import Actor, QNetwork, make_env |
|
|
| model_path = hf_hub_download( |
| repo_id="cleanrl/HalfCheetah-v4-td3_continuous_action-seed1", filename="td3_continuous_action.cleanrl_model" |
| ) |
| evaluate( |
| model_path, |
| make_env, |
| "HalfCheetah-v4", |
| eval_episodes=10, |
| run_name=f"eval", |
| Model=(Actor, QNetwork), |
| device="cpu", |
| capture_video=False, |
| ) |
|
|
