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| from easydict import EasyDict | |
| pendulum_ppo_config = dict( | |
| exp_name='pendulum_ppo_seed0', | |
| env=dict( | |
| collector_env_num=10, | |
| evaluator_env_num=5, | |
| act_scale=True, | |
| n_evaluator_episode=5, | |
| stop_value=-250, | |
| ), | |
| policy=dict( | |
| cuda=False, | |
| action_space='continuous', | |
| recompute_adv=True, | |
| model=dict( | |
| obs_shape=3, | |
| action_shape=1, | |
| encoder_hidden_size_list=[64, 64], | |
| action_space='continuous', | |
| actor_head_layer_num=0, | |
| critic_head_layer_num=0, | |
| sigma_type='conditioned', | |
| bound_type='tanh', | |
| ), | |
| learn=dict( | |
| epoch_per_collect=10, | |
| batch_size=32, | |
| learning_rate=3e-5, | |
| value_weight=0.5, | |
| entropy_weight=0.0, | |
| clip_ratio=0.2, | |
| adv_norm=False, | |
| value_norm=True, | |
| ignore_done=True, | |
| ), | |
| collect=dict( | |
| n_sample=200, | |
| unroll_len=1, | |
| discount_factor=0.9, | |
| gae_lambda=1., | |
| ), | |
| eval=dict(evaluator=dict(eval_freq=200, )) | |
| ), | |
| ) | |
| pendulum_ppo_config = EasyDict(pendulum_ppo_config) | |
| main_config = pendulum_ppo_config | |
| pendulum_ppo_create_config = dict( | |
| env=dict( | |
| type='pendulum', | |
| import_names=['dizoo.classic_control.pendulum.envs.pendulum_env'], | |
| ), | |
| env_manager=dict(type='base'), | |
| policy=dict(type='ppo'), | |
| ) | |
| pendulum_ppo_create_config = EasyDict(pendulum_ppo_create_config) | |
| create_config = pendulum_ppo_create_config | |
| if __name__ == "__main__": | |
| # or you can enter `ding -m serial_onpolicy -c pendulum_ppo_config.py -s 0` | |
| from ding.entry import serial_pipeline_onpolicy | |
| serial_pipeline_onpolicy([main_config, create_config], seed=0) | |