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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.
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_efficientzero_config = dict(
exp_name=f'data_ez_ctree/{env_name}_efficientzero_ns{num_simulations}_upc{update_per_collect}_rr{reanalyze_ratio}_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=256,
discrete_action_encoding_type='one_hot',
norm_type='BN',
),
policy_entropy_loss_weight=policy_entropy_loss_weight,
eps=dict(
eps_greedy_exploration_in_collect=eps_greedy_exploration_in_collect,
decay=int(2e5),
),
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,
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_efficientzero_config = EasyDict(minigrid_efficientzero_config)
main_config = minigrid_efficientzero_config
minigrid_efficientzero_create_config = dict(
env=dict(
type='minigrid_lightzero',
import_names=['zoo.minigrid.envs.minigrid_lightzero_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(
type='efficientzero',
import_names=['lzero.policy.efficientzero'],
),
collector=dict(
type='episode_muzero',
import_names=['lzero.worker.muzero_collector'],
)
)
minigrid_efficientzero_create_config = EasyDict(minigrid_efficientzero_create_config)
create_config = minigrid_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)
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