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from copy import deepcopy
from easydict import EasyDict
spaceinvaders_a2c_config = dict(
exp_name='spaceinvaders_a2c_seed0',
env=dict(
collector_env_num=16,
evaluator_env_num=8,
n_evaluator_episode=8,
stop_value=10000000000,
env_id='SpaceInvadersNoFrameskip-v4',
#'ALE/SpaceInvaders-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(
batch_size=80,
# (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=0.01,
grad_norm=0.5,
betas=(0.3, 0.99),
),
collect=dict(
# (int) collect n_sample data, train model 1 times
n_sample=80,
# (float) the trade-off factor lambda to balance 1step td and mc
gae_lambda=0.99,
discount_factor=0.99,
),
eval=dict(evaluator=dict(eval_freq=500, )),
),
)
spaceinvaders_a2c_config = EasyDict(spaceinvaders_a2c_config)
main_config = spaceinvaders_a2c_config
spaceinvaders_a2c_create_config = dict(
env=dict(
type='atari',
import_names=['dizoo.atari.envs.atari_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(type='a2c'),
replay_buffer=dict(type='naive'),
)
spaceinvaders_a2c_create_config = EasyDict(spaceinvaders_a2c_create_config)
create_config = spaceinvaders_a2c_create_config
if __name__ == '__main__':
# or you can enter ding -m serial_onpolicy -c spaceinvaders_a2c_config.py -s 0
from ding.entry import serial_pipeline_onpolicy
serial_pipeline_onpolicy((main_config, create_config), seed=0)
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