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from zoo.classic_control.cartpole.config.cartpole_muzero_config import main_config, create_config
from lzero.entry import eval_muzero
import numpy as np
if __name__ == "__main__":
"""
Entry point for the evaluation of the MuZero model on the CartPole 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 = 2
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
main_config.env.replay_path = './video'
for seed in seeds:
"""
- returns_mean (:obj:`float`): The mean return of the evaluation.
- returns (:obj:`List[float]`): The returns of the evaluation.
"""
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
)
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 evaluation results
print("=" * 20)
print(f"We evaluated a total of {len(seeds)} seeds. For each seed, we evaluated {num_episodes_each_seed} episode(s).")
print(f"For seeds {seeds}, the mean returns are {returns_mean_seeds}, and the returns are {returns_seeds}.")
print("Across all seeds, the mean reward is:", returns_mean_seeds.mean())
print("=" * 20) |