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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) | |