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| from easydict import EasyDict | |
| bipedalwalker_ddpg_config = dict( | |
| exp_name='bipedalwalker_ddpg_seed0', | |
| env=dict( | |
| env_id='BipedalWalker-v3', | |
| collector_env_num=8, | |
| evaluator_env_num=5, | |
| # (bool) Scale output action into legal range. | |
| act_scale=True, | |
| n_evaluator_episode=5, | |
| rew_clip=True, | |
| ), | |
| policy=dict( | |
| cuda=True, | |
| random_collect_size=10000, | |
| model=dict( | |
| obs_shape=24, | |
| action_shape=4, | |
| twin_critic=False, | |
| action_space='regression', | |
| actor_head_hidden_size=400, | |
| critic_head_hidden_size=400, | |
| ), | |
| learn=dict( | |
| update_per_collect=64, | |
| batch_size=256, | |
| learning_rate_actor=0.0003, | |
| learning_rate_critic=0.0003, | |
| target_theta=0.005, | |
| discount_factor=0.99, | |
| learner=dict(hook=dict(log_show_after_iter=1000, )) | |
| ), | |
| collect=dict(n_sample=64, ), | |
| other=dict(replay_buffer=dict(replay_buffer_size=300000, ), ), | |
| ), | |
| ) | |
| bipedalwalker_ddpg_config = EasyDict(bipedalwalker_ddpg_config) | |
| main_config = bipedalwalker_ddpg_config | |
| bipedalwalker_ddpg_create_config = dict( | |
| env=dict( | |
| type='bipedalwalker', | |
| import_names=['dizoo.box2d.bipedalwalker.envs.bipedalwalker_env'], | |
| ), | |
| env_manager=dict(type='subprocess'), | |
| policy=dict(type='ddpg'), | |
| ) | |
| bipedalwalker_ddpg_create_config = EasyDict(bipedalwalker_ddpg_create_config) | |
| create_config = bipedalwalker_ddpg_create_config | |
| if __name__ == "__main__": | |
| # or you can enter `ding -m serial -c bipedalwalker_ddpg_config.py -s 0` | |
| from ding.entry import serial_pipeline | |
| serial_pipeline([main_config, create_config], seed=0, max_env_step=int(1e5)) | |