File size: 2,060 Bytes
13621bf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | 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)
qf = Model[1](envs).to(device)
actor_params, qf_params = torch.load(model_path, map_location=device)
actor.load_state_dict(actor_params)
actor.eval()
qf.load_state_dict(qf_params)
qf.eval()
# note: qf is not used in this script
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.ddpg_continuous_action import Actor, QNetwork, make_env
model_path = hf_hub_download(
repo_id="cleanrl/HalfCheetah-v4-ddpg_continuous_action-seed1", filename="ddpg_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,
)
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