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| from easydict import EasyDict | |
| # ============================================================== | |
| # begin of the most frequently changed config specified by the user | |
| # ============================================================== | |
| collector_env_num = 8 | |
| n_episode = 8 | |
| evaluator_env_num = 3 | |
| num_simulations = 25 | |
| update_per_collect = 100 | |
| batch_size = 256 | |
| max_env_step = int(1e5) | |
| reanalyze_ratio = 0. | |
| # ============================================================== | |
| # end of the most frequently changed config specified by the user | |
| # ============================================================== | |
| cartpole_efficientzero_config = dict( | |
| exp_name= | |
| f'data_ez_ctree/cartpole_efficientzero_ns{num_simulations}_upc{update_per_collect}_rr{reanalyze_ratio}_seed0', | |
| env=dict( | |
| env_name='CartPole-v0', | |
| continuous=False, | |
| manually_discretization=False, | |
| collector_env_num=collector_env_num, | |
| evaluator_env_num=evaluator_env_num, | |
| n_evaluator_episode=evaluator_env_num, | |
| manager=dict(shared_memory=False, ), | |
| ), | |
| policy=dict( | |
| model=dict( | |
| observation_shape=4, | |
| action_space_size=2, | |
| model_type='mlp', | |
| lstm_hidden_size=128, | |
| latent_state_dim=128, | |
| discrete_action_encoding_type='one_hot', | |
| norm_type='BN', | |
| ), | |
| cuda=True, | |
| env_type='not_board_games', | |
| game_segment_length=50, | |
| update_per_collect=update_per_collect, | |
| batch_size=batch_size, | |
| optim_type='Adam', | |
| lr_piecewise_constant_decay=False, | |
| learning_rate=0.003, | |
| num_simulations=num_simulations, | |
| reanalyze_ratio=reanalyze_ratio, | |
| n_episode=n_episode, | |
| eval_freq=int(2e2), | |
| replay_buffer_size=int(1e6), # the size/capacity of replay_buffer, in the terms of transitions. | |
| collector_env_num=collector_env_num, | |
| evaluator_env_num=evaluator_env_num, | |
| ), | |
| ) | |
| cartpole_efficientzero_config = EasyDict(cartpole_efficientzero_config) | |
| main_config = cartpole_efficientzero_config | |
| cartpole_efficientzero_create_config = dict( | |
| env=dict( | |
| type='cartpole_lightzero', | |
| import_names=['zoo.classic_control.cartpole.envs.cartpole_lightzero_env'], | |
| ), | |
| env_manager=dict(type='subprocess'), | |
| policy=dict( | |
| type='efficientzero', | |
| import_names=['lzero.policy.efficientzero'], | |
| ), | |
| ) | |
| cartpole_efficientzero_create_config = EasyDict(cartpole_efficientzero_create_config) | |
| create_config = cartpole_efficientzero_create_config | |
| if __name__ == "__main__": | |
| # Users can use different train entry by specifying the entry_type. | |
| entry_type = "train_muzero" # options={"train_muzero", "train_muzero_with_gym_env"} | |
| if entry_type == "train_muzero": | |
| from lzero.entry import train_muzero | |
| elif entry_type == "train_muzero_with_gym_env": | |
| """ | |
| The ``train_muzero_with_gym_env`` entry means that the environment used in the training process is generated by wrapping the original gym environment with LightZeroEnvWrapper. | |
| Users can refer to lzero/envs/wrappers for more details. | |
| """ | |
| from lzero.entry import train_muzero_with_gym_env as train_muzero | |
| train_muzero([main_config, create_config], seed=0, max_env_step=max_env_step) | |