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from easydict import EasyDict
qbert_ppg_config = dict(
exp_name='qbert_ppg_seed0',
env=dict(
collector_env_num=8,
evaluator_env_num=8,
n_evaluator_episode=8,
stop_value=1000000,
env_id='QbertNoFrameskip-v4',
#'ALE/Qbert-v5' is available. But special setting is needed after gym make.
frame_stack=4
),
policy=dict(
cuda=True,
model=dict(
obs_shape=[4, 84, 84],
action_shape=6,
encoder_hidden_size_list=[32, 64, 64, 128],
actor_head_hidden_size=128,
critic_head_hidden_size=128,
critic_head_layer_num=2,
),
learn=dict(
update_per_collect=24,
batch_size=128,
# (bool) Whether to normalize advantage. Default to False.
adv_norm=False,
learning_rate=0.0001,
# (float) loss weight of the value network, the weight of policy network is set to 1
value_weight=0.5,
# (float) loss weight of the entropy regularization, the weight of policy network is set to 1
entropy_weight=0.03,
clip_ratio=0.1,
epochs_aux=6,
beta_weight=1,
aux_freq=100
),
collect=dict(
# (int) collect n_sample data, train model n_iteration times
n_sample=1024,
# (float) the trade-off factor lambda to balance 1step td and mc
gae_lambda=0.95,
discount_factor=0.99,
),
eval=dict(evaluator=dict(eval_freq=1000, )),
other=dict(
replay_buffer=dict(
multi_buffer=True,
policy=dict(
replay_buffer_size=100000,
max_use=3,
),
value=dict(
replay_buffer_size=100000,
max_use=10,
),
),
),
),
)
main_config = EasyDict(qbert_ppg_config)
qbert_ppg_create_config = dict(
env=dict(
type='atari',
import_names=['dizoo.atari.envs.atari_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(type='ppg_offpolicy'),
)
create_config = EasyDict(qbert_ppg_create_config)
if __name__ == '__main__':
# or you can enter ding -m serial -c qbert_ppg_config.py -s 0
from ding.entry import serial_pipeline
serial_pipeline((main_config, create_config), seed=0)
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