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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)
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