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from easydict import EasyDict
# options={'memory_len/0', 'memory_len/9', 'memory_len/17', 'memory_len/20', 'memory_len/22', 'memory_size/0', 'bsuite_swingup/0', 'bandit_noise/0'}
env_name = 'memory_len/9'
if env_name in ['memory_len/0', 'memory_len/9', 'memory_len/17', 'memory_len/20', 'memory_len/22']:
# the memory_length of above envs is 1, 10, 50, 80, 100, respectively.
action_space_size = 2
observation_shape = 3
elif env_name in ['bsuite_swingup/0']:
action_space_size = 3
observation_shape = 8
elif env_name == 'bandit_noise/0':
action_space_size = 11
observation_shape = 1
elif env_name in ['memory_size/0']:
action_space_size = 2
observation_shape = 3
else:
raise NotImplementedError
# ==============================================================
# 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 = False
K = 2 # num_of_sampled_actions
num_simulations = 50
update_per_collect = 100
batch_size = 256
max_env_step = int(5e5)
reanalyze_ratio = 0.
# ==============================================================
# end of the most frequently changed config specified by the user
# ==============================================================
bsuite_sampled_efficientzero_config = dict(
exp_name=
f'data_sez_ctree/bsuite_sampled_efficientzero_ns{num_simulations}_upc{update_per_collect}_rr{reanalyze_ratio}_seed{seed}',
env=dict(
env_name=env_name,
stop_value=int(1e6),
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=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=128,
latent_state_dim=128,
discrete_action_encoding_type='one_hot',
norm_type='BN',
),
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,
),
)
bsuite_sampled_efficientzero_config = EasyDict(bsuite_sampled_efficientzero_config)
main_config = bsuite_sampled_efficientzero_config
bsuite_sampled_efficientzero_create_config = dict(
env=dict(
type='bsuite_lightzero',
import_names=['zoo.bsuite.envs.bsuite_lightzero_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(
type='sampled_efficientzero',
import_names=['lzero.policy.sampled_efficientzero'],
),
collector=dict(
type='episode_muzero',
import_names=['lzero.worker.muzero_collector'],
)
)
bsuite_sampled_efficientzero_create_config = EasyDict(bsuite_sampled_efficientzero_create_config)
create_config = bsuite_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)
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