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from easydict import EasyDict
pong_sqil_config = dict(
exp_name='pong_sqil_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=True,
model=dict(
obs_shape=[4, 84, 84],
action_shape=6,
encoder_hidden_size_list=[128, 128, 512],
),
nstep=3,
discount_factor=0.97, # discount_factor: 0.97-0.99
learn=dict(update_per_collect=10, batch_size=32, learning_rate=0.0001, target_update_freq=500,
alpha=0.1), # alpha: 0.08-0.12
collect=dict(
n_sample=96,
# Users should add their own model path here. Model path should lead to a model.
# Absolute path is recommended.
# In DI-engine, it is ``exp_name/ckpt/ckpt_best.pth.tar``.
model_path='model_path_placeholder',
),
other=dict(
eps=dict(
type='exp',
start=1.,
end=0.05,
decay=250000,
),
replay_buffer=dict(replay_buffer_size=100000, ),
),
),
)
pong_sqil_config = EasyDict(pong_sqil_config)
main_config = pong_sqil_config
pong_sqil_create_config = dict(
env=dict(
type='atari',
import_names=['dizoo.atari.envs.atari_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(type='sql'),
)
pong_sqil_create_config = EasyDict(pong_sqil_create_config)
create_config = pong_sqil_create_config
if __name__ == '__main__':
# or you can enter `ding -m serial_sqil -c pong_sqil_config.py -s 0`
# then input the config you used to generate your expert model in the path mentioned above
# e.g. pong_dqn_config.py
from ding.entry import serial_pipeline_sqil
from dizoo.atari.config.serial.pong import pong_dqn_config, pong_dqn_create_config
expert_main_config = pong_dqn_config
expert_create_config = pong_dqn_create_config
serial_pipeline_sqil((main_config, create_config), (expert_main_config, expert_create_config), seed=0)
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