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from easydict import EasyDict
pong_cql_config = dict(
exp_name='pong_cql_seed0',
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],
num_quantiles=200,
),
nstep=1,
discount_factor=0.99,
learn=dict(
train_epoch=30000,
batch_size=32,
learning_rate=0.00005,
target_update_freq=2000,
min_q_weight=10.0,
),
collect=dict(
n_sample=100,
data_type='hdf5',
# Users should add their own data path here. Data path should lead to a file to store data or load the stored data.
# Absolute path is recommended.
# In DI-engine, it is usually located in ``exp_name`` directory
data_path='./default_experiment/expert.pkl',
),
eval=dict(evaluator=dict(eval_freq=4000, )),
other=dict(
eps=dict(
type='exp',
start=1.,
end=0.05,
decay=250000,
),
replay_buffer=dict(replay_buffer_size=100000, ),
),
),
)
pong_cql_config = EasyDict(pong_cql_config)
main_config = pong_cql_config
pong_cql_create_config = dict(
env=dict(
type='atari',
import_names=['dizoo.atari.envs.atari_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(type='cql_discrete'),
)
pong_cql_create_config = EasyDict(pong_cql_create_config)
create_config = pong_cql_create_config
if __name__ == '__main__':
# or you can enter `ding -m serial_offline -c pong_cql_config.py -s 0`
from ding.entry import serial_pipeline_offline
serial_pipeline_offline((main_config, create_config), seed=0)
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