| from stable_baselines3 import DQN | |
| from stable_baselines3.common.evaluation import evaluate_policy | |
| from stable_baselines3.common.monitor import Monitor | |
| import gymnasium as gym | |
| MODEL_NAME = "ALE-Pacman-v5-control" | |
| # the saved model does not contain the replay buffer | |
| loaded_model = DQN.load(MODEL_NAME) | |
| # print(f"The loaded_model has {loaded_model.replay_buffer.size()} transitions in its buffer") | |
| # now the loaded replay is not empty anymore | |
| # print(f"The loaded_model has {loaded_model.replay_buffer.size()} transitions in its buffer") | |
| # Retrieve the environment | |
| eval_env = Monitor(gym.make("ALE/Pacman-v5", render_mode="human", )) | |
| # Evaluate the policy | |
| mean_reward, std_reward = evaluate_policy(loaded_model.policy, eval_env, n_eval_episodes=10, deterministic=False, ) | |
| print(f"mean_reward={mean_reward:.2f} +/- {std_reward}") | |