| from easydict import EasyDict | |
| import ding.envs.gym_env | |
| cfg = dict( | |
| exp_name='Pendulum-v1-PG', | |
| seed=0, | |
| env=dict( | |
| env_id='Pendulum-v1', | |
| collector_env_num=8, | |
| evaluator_env_num=5, | |
| n_evaluator_episode=5, | |
| stop_value=-200, | |
| act_scale=True, | |
| ), | |
| policy=dict( | |
| cuda=False, | |
| action_space='continuous', | |
| model=dict( | |
| action_space='continuous', | |
| obs_shape=3, | |
| action_shape=1, | |
| ), | |
| learn=dict( | |
| batch_size=4000, | |
| learning_rate=0.001, | |
| entropy_weight=0.001, | |
| ), | |
| collect=dict( | |
| n_episode=20, | |
| unroll_len=1, | |
| discount_factor=0.99, | |
| ), | |
| eval=dict(evaluator=dict(eval_freq=1, )) | |
| ), | |
| wandb_logger=dict( | |
| gradient_logger=True, video_logger=True, plot_logger=True, action_logger=True, return_logger=False | |
| ), | |
| ) | |
| cfg = EasyDict(cfg) | |
| env = ding.envs.gym_env.env | |