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| from easydict import EasyDict | |
| # options={'Hopper-v3', 'HalfCheetah-v3', 'Walker2d-v3', 'Ant-v3', 'Humanoid-v3'} | |
| env_name = 'Hopper-v3' | |
| if env_name == 'Hopper-v3': | |
| action_space_size = 3 | |
| observation_shape = 11 | |
| elif env_name in ['HalfCheetah-v3', 'Walker2d-v3']: | |
| action_space_size = 6 | |
| observation_shape = 17 | |
| elif env_name == 'Ant-v3': | |
| action_space_size = 8 | |
| observation_shape = 111 | |
| elif env_name == 'Humanoid-v3': | |
| action_space_size = 17 | |
| observation_shape = 376 | |
| ignore_done = False | |
| if env_name == 'HalfCheetah-v3': | |
| # for halfcheetah, we ignore done signal to predict the Q value of the last step correctly. | |
| ignore_done = True | |
| # ============================================================== | |
| # begin of the most frequently changed config specified by the user | |
| # ============================================================== | |
| seed = 0 | |
| collector_env_num = 8 | |
| n_episode = 8 | |
| evaluator_env_num = 3 | |
| continuous_action_space = True | |
| K = 20 # num_of_sampled_actions | |
| num_simulations = 50 | |
| update_per_collect = 200 | |
| batch_size = 256 | |
| max_env_step = int(5e6) | |
| reanalyze_ratio = 0. | |
| policy_entropy_loss_weight = 0.005 | |
| # ============================================================== | |
| # end of the most frequently changed config specified by the user | |
| # ============================================================== | |
| mujoco_sampled_efficientzero_config = dict( | |
| exp_name= | |
| f'data_sez_ctree/{env_name[:-3]}_sampled_efficientzero_ns{num_simulations}_upc{update_per_collect}_rr{reanalyze_ratio}_bs-{batch_size}_pelw{policy_entropy_loss_weight}_seed{seed}', | |
| env=dict( | |
| env_name=env_name, | |
| action_clip=True, | |
| continuous=True, | |
| 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=observation_shape, | |
| action_space_size=action_space_size, | |
| continuous_action_space=continuous_action_space, | |
| num_of_sampled_actions=K, | |
| model_type='mlp', | |
| lstm_hidden_size=256, | |
| latent_state_dim=256, | |
| self_supervised_learning_loss=True, | |
| res_connection_in_dynamics=True, | |
| ), | |
| cuda=True, | |
| policy_entropy_loss_weight=policy_entropy_loss_weight, | |
| ignore_done=ignore_done, | |
| env_type='not_board_games', | |
| game_segment_length=200, | |
| update_per_collect=update_per_collect, | |
| batch_size=batch_size, | |
| discount_factor=0.997, | |
| optim_type='AdamW', | |
| lr_piecewise_constant_decay=False, | |
| learning_rate=0.003, | |
| grad_clip_value=0.5, | |
| num_simulations=num_simulations, | |
| reanalyze_ratio=reanalyze_ratio, | |
| n_episode=n_episode, | |
| eval_freq=int(2e3), | |
| replay_buffer_size=int(1e6), | |
| collector_env_num=collector_env_num, | |
| evaluator_env_num=evaluator_env_num, | |
| ), | |
| ) | |
| mujoco_sampled_efficientzero_config = EasyDict(mujoco_sampled_efficientzero_config) | |
| main_config = mujoco_sampled_efficientzero_config | |
| mujoco_sampled_efficientzero_create_config = dict( | |
| env=dict( | |
| type='mujoco_lightzero', | |
| import_names=['zoo.mujoco.envs.mujoco_lightzero_env'], | |
| ), | |
| env_manager=dict(type='subprocess'), | |
| policy=dict( | |
| type='sampled_efficientzero', | |
| import_names=['lzero.policy.sampled_efficientzero'], | |
| ), | |
| ) | |
| mujoco_sampled_efficientzero_create_config = EasyDict(mujoco_sampled_efficientzero_create_config) | |
| create_config = mujoco_sampled_efficientzero_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) | |