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| from easydict import EasyDict | |
| pendulum_ddpg_config = dict( | |
| exp_name='pendulum_ddpg_seed0', | |
| env=dict( | |
| collector_env_num=8, | |
| 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=800, | |
| model=dict( | |
| obs_shape=3, | |
| action_shape=1, | |
| twin_critic=False, | |
| action_space='regression', | |
| ), | |
| learn=dict( | |
| update_per_collect=2, | |
| batch_size=128, | |
| learning_rate_actor=0.001, | |
| learning_rate_critic=0.001, | |
| ignore_done=True, | |
| actor_update_freq=1, | |
| noise=False, | |
| ), | |
| collect=dict( | |
| n_sample=48, | |
| noise_sigma=0.1, | |
| collector=dict(collect_print_freq=1000, ), | |
| ), | |
| eval=dict(evaluator=dict(eval_freq=100, )), | |
| other=dict(replay_buffer=dict( | |
| replay_buffer_size=20000, | |
| max_use=16, | |
| ), ), | |
| ), | |
| ) | |
| pendulum_ddpg_config = EasyDict(pendulum_ddpg_config) | |
| main_config = pendulum_ddpg_config | |
| pendulum_ddpg_create_config = dict( | |
| env=dict( | |
| type='pendulum', | |
| import_names=['dizoo.classic_control.pendulum.envs.pendulum_env'], | |
| ), | |
| env_manager=dict(type='base'), | |
| policy=dict(type='ddpg'), | |
| ) | |
| pendulum_ddpg_create_config = EasyDict(pendulum_ddpg_create_config) | |
| create_config = pendulum_ddpg_create_config | |
| if __name__ == "__main__": | |
| # or you can enter `ding -m serial -c pendulum_ddpg_config.py -s 0` | |
| from ding.entry import serial_pipeline | |
| serial_pipeline([main_config, create_config], seed=0) | |