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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 = 50
update_per_collect = 200
batch_size = 256
max_env_step = int(5e6)
reanalyze_ratio = 0.
# ==============================================================
# end of the most frequently changed config specified by the user
# ==============================================================
lunarlander_muzero_config = dict(
exp_name=f'data_mz_ctree/lunarlander_muzero_ns{num_simulations}_upc{update_per_collect}_rr{reanalyze_ratio}_seed0',
env=dict(
env_name='LunarLander-v2',
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=8,
action_space_size=4,
model_type='mlp',
lstm_hidden_size=256,
latent_state_dim=256,
self_supervised_learning_loss=True, # NOTE: default is False.
discrete_action_encoding_type='one_hot',
res_connection_in_dynamics=True,
norm_type='BN',
),
cuda=True,
env_type='not_board_games',
game_segment_length=200,
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.
grad_clip_value=0.5,
num_simulations=num_simulations,
reanalyze_ratio=reanalyze_ratio,
n_episode=n_episode,
eval_freq=int(1e3),
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,
),
)
lunarlander_muzero_config = EasyDict(lunarlander_muzero_config)
main_config = lunarlander_muzero_config
lunarlander_muzero_create_config = dict(
env=dict(
type='lunarlander',
import_names=['zoo.box2d.lunarlander.envs.lunarlander_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(
type='muzero',
import_names=['lzero.policy.muzero'],
),
collector=dict(
type='episode_muzero',
get_train_sample=True,
import_names=['lzero.worker.muzero_collector'],
)
)
lunarlander_muzero_create_config = EasyDict(lunarlander_muzero_create_config)
create_config = lunarlander_muzero_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
train_muzero([main_config, create_config], seed=0, max_env_step=max_env_step)
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
train_muzero_with_gym_env([main_config, create_config], seed=0, max_env_step=max_env_step)
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