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| from easydict import EasyDict | |
| pendulum_sac_config = dict( | |
| exp_name='pendulum_sac_seed0', | |
| env=dict( | |
| collector_env_num=10, | |
| evaluator_env_num=5, | |
| # (bool) Scale output action into legal range. | |
| act_scale=True, | |
| n_evaluator_episode=5, | |
| stop_value=-250, | |
| ), | |
| policy=dict( | |
| cuda=False, | |
| priority=False, | |
| random_collect_size=1000, | |
| model=dict( | |
| obs_shape=3, | |
| action_shape=1, | |
| twin_critic=True, | |
| action_space='reparameterization', | |
| actor_head_hidden_size=128, | |
| critic_head_hidden_size=128, | |
| ), | |
| learn=dict( | |
| update_per_collect=1, | |
| batch_size=128, | |
| learning_rate_q=0.001, | |
| learning_rate_policy=0.001, | |
| learning_rate_alpha=0.0003, | |
| ignore_done=True, | |
| target_theta=0.005, | |
| discount_factor=0.99, | |
| auto_alpha=True, | |
| ), | |
| collect=dict(n_sample=10, ), | |
| eval=dict(evaluator=dict(eval_freq=100, )), | |
| other=dict(replay_buffer=dict(replay_buffer_size=100000, ), ), | |
| ), | |
| ) | |
| pendulum_sac_config = EasyDict(pendulum_sac_config) | |
| main_config = pendulum_sac_config | |
| pendulum_sac_create_config = dict( | |
| env=dict( | |
| type='pendulum', | |
| import_names=['dizoo.classic_control.pendulum.envs.pendulum_env'], | |
| ), | |
| env_manager=dict(type='base'), | |
| policy=dict(type='sac'), | |
| ) | |
| pendulum_sac_create_config = EasyDict(pendulum_sac_create_config) | |
| create_config = pendulum_sac_create_config | |
| if __name__ == "__main__": | |
| # or you can enter `ding -m serial -c pendulum_sac_config.py -s 0` | |
| from ding.entry import serial_pipeline | |
| serial_pipeline([main_config, create_config], seed=0) | |