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| from easydict import EasyDict | |
| qbert_acer_config = dict( | |
| exp_name='qbert_acer_seed0', | |
| env=dict( | |
| collector_env_num=16, | |
| evaluator_env_num=8, | |
| n_evaluator_episode=8, | |
| stop_value=int(1e6), | |
| env_id='QbertNoFrameskip-v4', | |
| #'ALE/Qbert-v5' is available. But special setting is needed after gym make. | |
| frame_stack=4, | |
| manager=dict(shared_memory=False, ) | |
| ), | |
| 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', | |
| 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=0.99, | |
| # (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=0.99, | |
| collector=dict(collect_print_freq=1000, ), | |
| ), | |
| eval=dict(evaluator=dict(eval_freq=1000, )), | |
| other=dict(replay_buffer=dict(replay_buffer_size=3000, ), ), | |
| ), | |
| ) | |
| main_config = EasyDict(qbert_acer_config) | |
| qbert_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(qbert_acer_create_config) | |
| if __name__ == "__main__": | |
| # or you can enter ding -m serial -c qbert_acer_config.py -s 0 | |
| from ding.entry import serial_pipeline | |
| serial_pipeline([main_config, create_config], seed=0) | |