Spaces:
Sleeping
Sleeping
| from easydict import EasyDict | |
| halfcheetah_bdq_config = dict( | |
| exp_name='halfcheetah_bdq_seed0', | |
| env=dict( | |
| env_id='HalfCheetah-v3', | |
| norm_reward=dict(use_norm=False, ), | |
| collector_env_num=8, | |
| evaluator_env_num=8, | |
| n_evaluator_episode=8, | |
| stop_value=12000, | |
| action_bins_per_branch=2, | |
| ), | |
| policy=dict( | |
| cuda=False, | |
| priority=False, | |
| discount_factor=0.99, | |
| nstep=1, | |
| model=dict( | |
| obs_shape=17, | |
| num_branches=6, | |
| action_bins_per_branch=2, # mean the action shape is 6, 2 discrete actions for each action dimension | |
| encoder_hidden_size_list=[256, 256, 128], | |
| ), | |
| learn=dict( | |
| batch_size=512, | |
| learning_rate=3e-4, | |
| ignore_done=True, | |
| target_update_freq=500, | |
| update_per_collect=20, | |
| ), | |
| collect=dict( | |
| n_sample=256, | |
| unroll_len=1, | |
| ), | |
| eval=dict(evaluator=dict(eval_freq=1000, )), | |
| other=dict( | |
| # Epsilon greedy with decay. | |
| eps=dict( | |
| # Decay type. Support ['exp', 'linear']. | |
| type='exp', | |
| start=1, | |
| end=0.05, | |
| decay=int(1e5), | |
| ), | |
| replay_buffer=dict(replay_buffer_size=int(1e6), ) | |
| ), | |
| ), | |
| ) | |
| halfcheetah_bdq_config = EasyDict(halfcheetah_bdq_config) | |
| main_config = halfcheetah_bdq_config | |
| halfcheetah_bdq_create_config = dict( | |
| env=dict( | |
| type='mujoco', | |
| import_names=['dizoo.mujoco.envs.mujoco_env'], | |
| ), | |
| env_manager=dict(type='subprocess'), | |
| policy=dict(type='bdq', ), | |
| ) | |
| halfcheetah_bdq_create_config = EasyDict(halfcheetah_bdq_create_config) | |
| create_config = halfcheetah_bdq_create_config | |
| if __name__ == "__main__": | |
| # or you can enter `ding -m serial_onpolicy -c halfcheetah_onbdq_config.py -s 0` | |
| from ding.entry import serial_pipeline | |
| serial_pipeline( | |
| (main_config, create_config), | |
| seed=0, | |
| max_env_step=10000000, | |
| ) | |