| from easydict import EasyDict | |
| import ding.envs.gym_env | |
| cfg = dict( | |
| exp_name='Pendulum-v1-DDPG', | |
| seed=0, | |
| env=dict( | |
| env_id='Pendulum-v1', | |
| collector_env_num=8, | |
| evaluator_env_num=5, | |
| n_evaluator_episode=5, | |
| stop_value=-250, | |
| act_scale=True, | |
| ), | |
| 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, | |
| ), ), | |
| ), | |
| 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 | |