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| from easydict import EasyDict | |
| walker_ddpg_config = dict( | |
| exp_name='evogym_walker_ddpg_seed0', | |
| env=dict( | |
| env_id='Walker-v0', | |
| robot='speed_bot', | |
| robot_dir='./dizoo/evogym/envs', | |
| collector_env_num=8, | |
| evaluator_env_num=8, | |
| n_evaluator_episode=8, | |
| stop_value=10, | |
| manager=dict(shared_memory=True, ), | |
| # The path to save the game replay | |
| # replay_path='./evogym_walker_ddpg_seed0/video', | |
| ), | |
| policy=dict( | |
| cuda=True, | |
| # load_path="./evogym_walker_ddpg_seed0/ckpt/ckpt_best.pth.tar", | |
| random_collect_size=1000, | |
| model=dict( | |
| obs_shape=58, | |
| action_shape=10, | |
| twin_critic=False, | |
| actor_head_hidden_size=256, | |
| critic_head_hidden_size=256, | |
| action_space='regression', | |
| ), | |
| learn=dict( | |
| update_per_collect=1, | |
| batch_size=256, | |
| learning_rate_actor=1e-3, | |
| learning_rate_critic=1e-3, | |
| ignore_done=False, | |
| target_theta=0.005, | |
| discount_factor=0.99, # discount_factor: 0.97-0.99 | |
| actor_update_freq=1, | |
| noise=False, | |
| ), | |
| collect=dict( | |
| n_sample=1, | |
| unroll_len=1, | |
| noise_sigma=0.1, | |
| ), | |
| other=dict(replay_buffer=dict(replay_buffer_size=1000000, ), ), | |
| ) | |
| ) | |
| walker_ddpg_config = EasyDict(walker_ddpg_config) | |
| main_config = walker_ddpg_config | |
| walker_ddpg_create_config = dict( | |
| env=dict( | |
| type='evogym', | |
| import_names=['dizoo.evogym.envs.evogym_env'], | |
| ), | |
| env_manager=dict(type='subprocess'), | |
| policy=dict( | |
| type='ddpg', | |
| import_names=['ding.policy.ddpg'], | |
| ), | |
| replay_buffer=dict(type='naive', ), | |
| ) | |
| walker_ddpg_create_config = EasyDict(walker_ddpg_create_config) | |
| create_config = walker_ddpg_create_config | |
| if __name__ == "__main__": | |
| # or you can enter `ding -m serial -c evogym_walker_ddpg_config.py -s 0 --env-step 1e7` | |
| from ding.entry import serial_pipeline | |
| serial_pipeline((main_config, create_config), seed=0) | |