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| from easydict import EasyDict | |
| # The typical MiniGrid env id: {'MiniGrid-Empty-8x8-v0', 'MiniGrid-FourRooms-v0', 'MiniGrid-DoorKey-8x8-v0','MiniGrid-DoorKey-16x16-v0'}, | |
| # please refer to https://github.com/Farama-Foundation/MiniGrid for details. | |
| env_name = 'MiniGrid-Empty-8x8-v0' | |
| max_env_step = int(1e6) | |
| # ============================================================== | |
| # begin of the most frequently changed config specified by the user | |
| # ============================================================== | |
| seed = 0 | |
| collector_env_num = 8 | |
| n_episode = 8 | |
| evaluator_env_num = 3 | |
| num_simulations = 50 | |
| update_per_collect = 200 | |
| batch_size = 256 | |
| reanalyze_ratio = 0 | |
| td_steps = 5 | |
| policy_entropy_loss_weight = 0. # 0.005 | |
| threshold_training_steps_for_final_temperature = int(5e5) | |
| eps_greedy_exploration_in_collect = False | |
| # ============================================================== | |
| # end of the most frequently changed config specified by the user | |
| # ============================================================== | |
| minigrid_muzero_config = dict( | |
| exp_name=f'data_mz_ctree/{env_name}_muzero_ns{num_simulations}_upc{update_per_collect}_rr{reanalyze_ratio}_' | |
| f'collect-eps-{eps_greedy_exploration_in_collect}_temp-final-steps-{threshold_training_steps_for_final_temperature}_pelw{policy_entropy_loss_weight}_seed{seed}', | |
| env=dict( | |
| stop_value=int(1e6), | |
| env_name=env_name, | |
| 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=2835, | |
| action_space_size=7, | |
| model_type='mlp', | |
| lstm_hidden_size=256, | |
| latent_state_dim=512, | |
| discrete_action_encoding_type='one_hot', | |
| norm_type='BN', | |
| self_supervised_learning_loss=True, # NOTE: default is False. | |
| ), | |
| eps=dict( | |
| eps_greedy_exploration_in_collect=eps_greedy_exploration_in_collect, | |
| decay=int(2e5), | |
| ), | |
| policy_entropy_loss_weight=policy_entropy_loss_weight, | |
| td_steps=td_steps, | |
| manual_temperature_decay=True, | |
| threshold_training_steps_for_final_temperature=threshold_training_steps_for_final_temperature, | |
| 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, | |
| ssl_loss_weight=2, # NOTE: default is 0. | |
| 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, | |
| ), | |
| ) | |
| minigrid_muzero_config = EasyDict(minigrid_muzero_config) | |
| main_config = minigrid_muzero_config | |
| minigrid_muzero_create_config = dict( | |
| env=dict( | |
| type='minigrid_lightzero', | |
| import_names=['zoo.minigrid.envs.minigrid_lightzero_env'], | |
| ), | |
| env_manager=dict(type='subprocess'), | |
| policy=dict( | |
| type='muzero', | |
| import_names=['lzero.policy.muzero'], | |
| ), | |
| collector=dict( | |
| type='episode_muzero', | |
| import_names=['lzero.worker.muzero_collector'], | |
| ) | |
| ) | |
| minigrid_muzero_create_config = EasyDict(minigrid_muzero_create_config) | |
| create_config = minigrid_muzero_create_config | |
| if __name__ == "__main__": | |
| from lzero.entry import train_muzero | |
| train_muzero([main_config, create_config], seed=seed, max_env_step=max_env_step) | |