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| from easydict import EasyDict | |
| qbert_a2c_config = dict( | |
| exp_name='qbert_a2c_seed0', | |
| env=dict( | |
| collector_env_num=16, | |
| evaluator_env_num=8, | |
| n_evaluator_episode=8, | |
| stop_value=1000000, | |
| env_id='QbertNoFrameskip-v4', | |
| #'ALE/Qbert-v5' is available. But special setting is needed after gym make. | |
| frame_stack=4 | |
| ), | |
| policy=dict( | |
| cuda=True, | |
| model=dict( | |
| obs_shape=[4, 84, 84], | |
| action_shape=6, | |
| encoder_hidden_size_list=[32, 64, 64, 256], | |
| actor_head_hidden_size=256, | |
| critic_head_hidden_size=256, | |
| critic_head_layer_num=2, | |
| ), | |
| learn=dict( | |
| batch_size=300, | |
| # (bool) Whether to normalize advantage. Default to False. | |
| adv_norm=False, | |
| learning_rate=0.0001414, | |
| # (float) loss weight of the value network, the weight of policy network is set to 1 | |
| value_weight=0.5, | |
| # (float) loss weight of the entropy regularization, the weight of policy network is set to 1 | |
| entropy_weight=0.01, | |
| grad_norm=0.5, | |
| betas=(0.0, 0.99), | |
| ), | |
| collect=dict( | |
| # (int) collect n_sample data, train model 1 times | |
| n_sample=160, | |
| # (float) the trade-off factor lambda to balance 1step td and mc | |
| gae_lambda=0.99, | |
| discount_factor=0.99, | |
| ), | |
| eval=dict(evaluator=dict(eval_freq=500, )), | |
| ), | |
| ) | |
| main_config = EasyDict(qbert_a2c_config) | |
| qbert_a2c_create_config = dict( | |
| env=dict( | |
| type='atari', | |
| import_names=['dizoo.atari.envs.atari_env'], | |
| ), | |
| env_manager=dict(type='subprocess'), | |
| policy=dict(type='a2c'), | |
| replay_buffer=dict(type='naive'), | |
| ) | |
| create_config = EasyDict(qbert_a2c_create_config) | |
| if __name__ == '__main__': | |
| # or you can enter ding -m serial_onpolicy -c qbert_a2c_config.py -s 0 | |
| from ding.entry import serial_pipeline_onpolicy | |
| serial_pipeline_onpolicy((main_config, create_config), seed=0) | |