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| # According to the model you want to evaluate, import the corresponding config. | |
| import numpy as np | |
| from lzero.entry import eval_muzero | |
| from zoo.game_2048.config.muzero_2048_config import main_config, create_config | |
| from zoo.game_2048.config.stochastic_muzero_2048_config import main_config, create_config | |
| if __name__ == "__main__": | |
| """ | |
| Entry point for the evaluation of the muzero or stochastic_muzero model on the 2048 environment. | |
| Variables: | |
| - model_path (:obj:`Optional[str]`): The pretrained model path, which should point to the ckpt file of the | |
| pretrained model. An absolute path is recommended. In LightZero, the path is usually something like | |
| ``exp_name/ckpt/ckpt_best.pth.tar``. | |
| - returns_mean_seeds (:obj:`List[float]`): List to store the mean returns for each seed. | |
| - returns_seeds (:obj:`List[float]`): List to store the returns for each seed. | |
| - seeds (:obj:`List[int]`): List of seeds for the environment. | |
| - num_episodes_each_seed (:obj:`int`): Number of episodes to run for each seed. | |
| - total_test_episodes (:obj:`int`): Total number of test episodes, computed as the product of the number of | |
| seeds and the number of episodes per seed. | |
| """ | |
| # model_path = './ckpt/ckpt_best.pth.tar' | |
| model_path = None | |
| returns_mean_seeds = [] | |
| returns_seeds = [] | |
| seeds = [0] | |
| num_episodes_each_seed = 1 | |
| # main_config.env.render_mode = 'image_realtime_mode' | |
| main_config.env.render_mode = 'image_savefile_mode' | |
| main_config.env.replay_path = './video' | |
| main_config.env.replay_format = 'gif' | |
| main_config.env.replay_name_suffix = 'muzero_ns100_s0' | |
| # main_config.env.replay_name_suffix = 'stochastic_muzero_ns100_s0' | |
| main_config.env.max_episode_steps = int(1e9) # Adjust according to different environments | |
| total_test_episodes = num_episodes_each_seed * len(seeds) | |
| create_config.env_manager.type = 'base' # Visualization requires the 'type' to be set as base | |
| main_config.env.evaluator_env_num = 1 # Visualization requires the 'env_num' to be set as 1 | |
| main_config.env.n_evaluator_episode = total_test_episodes | |
| for seed in seeds: | |
| returns_mean, returns = eval_muzero( | |
| [main_config, create_config], | |
| seed=seed, | |
| num_episodes_each_seed=num_episodes_each_seed, | |
| print_seed_details=False, | |
| model_path=model_path | |
| ) | |
| print(returns_mean, returns) | |
| returns_mean_seeds.append(returns_mean) | |
| returns_seeds.append(returns) | |
| returns_mean_seeds = np.array(returns_mean_seeds) | |
| returns_seeds = np.array(returns_seeds) | |
| print("=" * 20) | |
| print(f'We eval total {len(seeds)} seeds. In each seed, we eval {num_episodes_each_seed} episodes.') | |
| print(f'In seeds {seeds}, returns_mean_seeds is {returns_mean_seeds}, returns is {returns_seeds}') | |
| print('In all seeds, reward_mean:', returns_mean_seeds.mean()) | |
| print("=" * 20) | |