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from easydict import EasyDict
cartpole_bco_config = dict(
exp_name='cartpole_bco_seed0',
env=dict(
collector_env_num=8,
evaluator_env_num=5,
n_evaluator_episode=5,
stop_value=195,
replay_path=None,
),
policy=dict(
cuda=True,
continuous=False,
loss_type='l1_loss',
model=dict(
obs_shape=4,
action_shape=2,
encoder_hidden_size_list=[128, 128, 64],
dueling=True,
),
learn=dict(
train_epoch=20,
batch_size=128,
learning_rate=0.001,
weight_decay=1e-4,
momentum=0.9,
decay_epoch=30,
decay_rate=1,
warmup_lr=1e-4,
warmup_epoch=3,
optimizer='SGD',
lr_decay=True,
),
collect=dict(
n_episode=10,
# control the number (alpha*n_episode) of post-demonstration environment interactions at each iteration.
# Notice: alpha * n_episode > collector_env_num
model_path='abs model path', # epxert model path
data_path='abs data path', # expert data path
),
eval=dict(evaluator=dict(eval_freq=40, )),
other=dict(eps=dict(
type='exp',
start=0.95,
end=0.1,
decay=10000,
), )
),
bco=dict(
learn=dict(idm_batch_size=32, 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=0.8,
)
)
cartpole_bco_config = EasyDict(cartpole_bco_config)
main_config = cartpole_bco_config
cartpole_bco_create_config = dict(
env=dict(
type='cartpole',
import_names=['dizoo.classic_control.cartpole.envs.cartpole_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(type='bc'),
collector=dict(type='episode')
)
cartpole_bco_create_config = EasyDict(cartpole_bco_create_config)
create_config = cartpole_bco_create_config
if __name__ == "__main__":
from ding.entry import serial_pipeline_bco
from dizoo.classic_control.cartpole.config import cartpole_dqn_config, cartpole_dqn_create_config
expert_main_config = cartpole_dqn_config
expert_create_config = cartpole_dqn_create_config
serial_pipeline_bco(
[main_config, create_config], [cartpole_dqn_config, cartpole_dqn_create_config], seed=0, max_env_step=100000
)
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