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| from easydict import EasyDict | |
| halfcheetah_d4pg_config = dict( | |
| exp_name='halfcheetah_d4pg_seed0', | |
| env=dict( | |
| env_id='HalfCheetah-v3', | |
| norm_obs=dict(use_norm=False, ), | |
| norm_reward=dict(use_norm=False, ), | |
| collector_env_num=4, | |
| evaluator_env_num=4, | |
| n_evaluator_episode=8, | |
| stop_value=20000, | |
| ), | |
| policy=dict( | |
| cuda=True, | |
| priority=True, | |
| nstep=5, | |
| random_collect_size=10000, | |
| model=dict( | |
| obs_shape=17, | |
| action_shape=6, | |
| actor_head_hidden_size=512, | |
| critic_head_hidden_size=512, | |
| action_space='regression', | |
| critic_head_type='categorical', | |
| v_min=0, | |
| v_max=5000, # v_max: [3000, 10000] | |
| n_atom=51, | |
| ), | |
| learn=dict( | |
| update_per_collect=4, # update_per_collect: [1, 4] | |
| batch_size=256, | |
| learning_rate_actor=3e-4, | |
| learning_rate_critic=3e-4, | |
| ignore_done=True, | |
| target_theta=0.005, | |
| discount_factor=0.99, | |
| actor_update_freq=1, | |
| noise=False, | |
| ), | |
| collect=dict( | |
| n_sample=8, | |
| unroll_len=1, | |
| noise_sigma=0.2, # noise_sigma: [0.1, 0.2] | |
| ), | |
| other=dict(replay_buffer=dict(replay_buffer_size=1000000, ), ), | |
| ) | |
| ) | |
| halfcheetah_d4pg_config = EasyDict(halfcheetah_d4pg_config) | |
| main_config = halfcheetah_d4pg_config | |
| halfcheetah_d4pg_create_config = dict( | |
| env=dict( | |
| type='mujoco', | |
| import_names=['dizoo.mujoco.envs.mujoco_env'], | |
| ), | |
| env_manager=dict(type='subprocess'), | |
| policy=dict( | |
| type='d4pg', | |
| import_names=['ding.policy.d4pg'], | |
| ), | |
| ) | |
| halfcheetah_d4pg_create_config = EasyDict(halfcheetah_d4pg_create_config) | |
| create_config = halfcheetah_d4pg_create_config | |
| if __name__ == "__main__": | |
| # or you can enter `ding -m serial -c halfcheetah_d4pg_config.py -s 0` | |
| from ding.entry import serial_pipeline | |
| serial_pipeline((main_config, create_config), seed=0) | |