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from easydict import EasyDict
# ==============================================================
# begin of the most frequently changed config specified by the user
# ==============================================================
env_name = 'game_2048'
action_space_size = 4
use_ture_chance_label_in_chance_encoder = True
collector_env_num = 8
n_episode = 8
evaluator_env_num = 3
num_simulations = 100
update_per_collect = 200
batch_size = 512
max_env_step = int(1e9)
reanalyze_ratio = 0.
num_of_possible_chance_tile = 2
chance_space_size = 16 * num_of_possible_chance_tile
# ==============================================================
# end of the most frequently changed config specified by the user
# ==============================================================
game_2048_stochastic_muzero_config = dict(
exp_name=f'data_stochastic_mz_ctree/game_2048_npct-{num_of_possible_chance_tile}_stochastic_muzero_ns{num_simulations}_upc{update_per_collect}_rr{reanalyze_ratio}_bs{batch_size}_chance-{use_ture_chance_label_in_chance_encoder}_sslw2_seed0',
env=dict(
stop_value=int(1e6),
env_name=env_name,
obs_shape=(16, 4, 4),
obs_type='dict_encoded_board',
num_of_possible_chance_tile=num_of_possible_chance_tile,
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=(16, 4, 4),
action_space_size=action_space_size,
chance_space_size=chance_space_size,
image_channel=16,
# NOTE: whether to use the self_supervised_learning_loss. default is False
self_supervised_learning_loss=True,
discrete_action_encoding_type='one_hot',
norm_type='BN',
),
use_ture_chance_label_in_chance_encoder=use_ture_chance_label_in_chance_encoder,
mcts_ctree=True,
cuda=True,
game_segment_length=200,
update_per_collect=update_per_collect,
batch_size=batch_size,
td_steps=10,
discount_factor=0.999,
manual_temperature_decay=True,
optim_type='Adam',
lr_piecewise_constant_decay=False,
learning_rate=0.003,
weight_decay=1e-4,
num_simulations=num_simulations,
reanalyze_ratio=reanalyze_ratio,
ssl_loss_weight=2, # default is 0
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,
),
)
game_2048_stochastic_muzero_config = EasyDict(game_2048_stochastic_muzero_config)
main_config = game_2048_stochastic_muzero_config
game_2048_stochastic_muzero_create_config = dict(
env=dict(
type='game_2048',
import_names=['zoo.game_2048.envs.game_2048_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(
type='stochastic_muzero',
import_names=['lzero.policy.stochastic_muzero'],
),
)
game_2048_stochastic_muzero_create_config = EasyDict(game_2048_stochastic_muzero_create_config)
create_config = game_2048_stochastic_muzero_create_config
if __name__ == "__main__":
from lzero.entry import train_muzero
train_muzero([main_config, create_config], seed=0, max_env_step=max_env_step)
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