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from zoo.board_games.connect4.config.connect4_muzero_bot_mode_config import main_config, create_config |
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from lzero.entry import eval_muzero |
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import numpy as np |
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if __name__ == '__main__': |
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""" |
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Entry point for the evaluation of the MuZero model on the Connect4 environment. |
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Variables: |
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- model_path (:obj:`Optional[str]`): The pretrained model path, which should point to the ckpt file of the |
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pretrained model. An absolute path is recommended. In LightZero, the path is usually something like |
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``exp_name/ckpt/ckpt_best.pth.tar``. |
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- returns_mean_seeds (:obj:`List[float]`): List to store the mean returns for each seed. |
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- returns_seeds (:obj:`List[float]`): List to store the returns for each seed. |
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- seeds (:obj:`List[int]`): List of seeds for the environment. |
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- num_episodes_each_seed (:obj:`int`): Number of episodes to run for each seed. |
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- total_test_episodes (:obj:`int`): Total number of test episodes, computed as the product of the number of |
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seeds and the number of episodes per seed. |
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""" |
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model_path = None |
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seeds = [0] |
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num_episodes_each_seed = 1 |
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main_config.env.agent_vs_human = False |
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main_config.env.render_mode = 'image_savefile_mode' |
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main_config.env.replay_path = './video' |
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main_config.env.prob_random_action_in_bot = 0. |
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main_config.env.bot_action_type = 'rule' |
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create_config.env_manager.type = 'base' |
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main_config.env.evaluator_env_num = 1 |
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main_config.env.n_evaluator_episode = 1 |
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total_test_episodes = num_episodes_each_seed * len(seeds) |
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returns_mean_seeds = [] |
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returns_seeds = [] |
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for seed in seeds: |
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returns_mean, returns = eval_muzero( |
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[main_config, create_config], |
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seed=seed, |
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num_episodes_each_seed=num_episodes_each_seed, |
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print_seed_details=True, |
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model_path=model_path |
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) |
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returns_mean_seeds.append(returns_mean) |
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returns_seeds.append(returns) |
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returns_mean_seeds = np.array(returns_mean_seeds) |
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returns_seeds = np.array(returns_seeds) |
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print("=" * 20) |
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print(f"We evaluated a total of {len(seeds)} seeds. For each seed, we evaluated {num_episodes_each_seed} episode(s).") |
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print(f"For seeds {seeds}, the mean returns are {returns_mean_seeds}, and the returns are {returns_seeds}.") |
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print("Across all seeds, the mean reward is:", returns_mean_seeds.mean()) |
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print( |
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f'win rate: {len(np.where(returns_seeds == 1.)[0]) / total_test_episodes}, draw rate: {len(np.where(returns_seeds == 0.)[0]) / total_test_episodes}, lose rate: {len(np.where(returns_seeds == -1.)[0]) / total_test_episodes}' |
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) |
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print("=" * 20) |
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