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from easydict import EasyDict
pong_acer_config = dict(
env=dict(
collector_env_num=8,
evaluator_env_num=8,
n_evaluator_episode=8,
stop_value=20,
env_id='PongNoFrameskip-v4',
#'ALE/Pong-v5' is available. But special setting is needed after gym make.
frame_stack=4,
),
policy=dict(
cuda=True,
priority=False,
model=dict(
obs_shape=[4, 84, 84],
action_shape=6,
encoder_hidden_size_list=[128, 128, 512],
critic_head_hidden_size=512,
critic_head_layer_num=2,
actor_head_hidden_size=512,
actor_head_layer_num=2,
),
unroll_len=64,
learn=dict(
# (int) collect n_sample data, train model update_per_collect times
# here we follow impala serial pipeline
update_per_collect=10,
# (int) the number of data for a train iteration
batch_size=64,
# grad_clip_type='clip_norm',
# clip_value=10,
learning_rate_actor=0.0001,
learning_rate_critic=0.0003,
# (float) loss weight of the entropy regularization, the weight of policy network is set to 1
entropy_weight=0.01,
# (float) discount factor for future reward, defaults int [0, 1]
discount_factor=0.9,
# (float) additional discounting parameter
trust_region=True,
# (float) clip ratio of importance weights
c_clip_ratio=10,
),
collect=dict(
# (int) collect n_sample data, train model n_iteration times
n_sample=64,
# (float) discount factor for future reward, defaults int [0, 1]
discount_factor=0.9,
collector=dict(collect_print_freq=1000, ),
),
eval=dict(evaluator=dict(eval_freq=5000, )),
other=dict(replay_buffer=dict(replay_buffer_size=3000, ), ),
),
)
main_config = EasyDict(pong_acer_config)
pong_acer_create_config = dict(
env=dict(
type='atari',
import_names=['dizoo.atari.envs.atari_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(type='acer'),
)
create_config = EasyDict(pong_acer_create_config)
if __name__ == '__main__':
# or you can enter `ding -m serial -c pong_acer_config.py -s 0`
from ding.entry import serial_pipeline
serial_pipeline((main_config, create_config), seed=0)
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