gomoku / DI-engine /dizoo /box2d /lunarlander /config /lunarlander_bco_config.py
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from easydict import EasyDict
nstep = 3
lunarlander_bco_config = dict(
exp_name='lunarlander_bco_seed0',
env=dict(
collector_env_num=8,
evaluator_env_num=8,
env_id='LunarLander-v2',
n_evaluator_episode=8,
stop_value=200,
),
policy=dict(
# Whether to use cuda for network.
cuda=True,
continuous=False,
loss_type='l1_loss',
model=dict(
obs_shape=8,
action_shape=4,
encoder_hidden_size_list=[512, 64],
# Whether to use dueling head.
dueling=True,
),
# learn_mode config
learn=dict(
update_per_collect=10,
train_epoch=20,
batch_size=64,
learning_rate=0.001,
weight_decay=1e-4,
decay_epoch=1000,
decay_rate=0.5,
warmup_lr=1e-4,
warmup_epoch=3,
optimizer='SGD',
lr_decay=True,
momentum=0.9,
),
# collect_mode config
collect=dict(
n_episode=100,
model_path='abs model path', # expert model path
data_path='abs data path', # expert data path
),
# eval_mode config
eval=dict(evaluator=dict(eval_freq=50, )),
# command_mode config
other=dict(
# Epsilon greedy with decay.
eps=dict(
# Decay type. Support ['exp', 'linear'].
type='exp',
start=0.95,
end=0.1,
decay=50000,
),
replay_buffer=dict(replay_buffer_size=100000, )
),
),
bco=dict(
learn=dict(idm_batch_size=256, idm_learning_rate=0.001, idm_weight_decay=1e-4, idm_train_epoch=10),
model=dict(idm_encoder_hidden_size_list=[60, 80, 100, 40], action_space='discrete'),
alpha=0.2,
)
)
lunarlander_bco_config = EasyDict(lunarlander_bco_config)
main_config = lunarlander_bco_config
lunarlander_bco_create_config = dict(
env=dict(
type='lunarlander',
import_names=['dizoo.box2d.lunarlander.envs.lunarlander_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(type='bc'),
collector=dict(type='episode'),
)
lunarlander_bco_create_config = EasyDict(lunarlander_bco_create_config)
create_config = lunarlander_bco_create_config
if __name__ == "__main__":
from ding.entry import serial_pipeline_bco
from dizoo.box2d.lunarlander.config import lunarlander_dqn_config, lunarlander_dqn_create_config
expert_main_config = lunarlander_dqn_config
expert_create_config = lunarlander_dqn_create_config
serial_pipeline_bco(
[main_config, create_config], [expert_main_config, expert_create_config], seed=0, max_env_step=2000000
)