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| from easydict import EasyDict | |
| from copy import deepcopy | |
| halfcheetah_dt_config = dict( | |
| exp_name='dt_log/d4rl/halfcheetah/halfcheetah_medium_expert_dt_seed0', | |
| env=dict( | |
| env_id='HalfCheetah-v3', | |
| collector_env_num=1, | |
| evaluator_env_num=8, | |
| use_act_scale=True, | |
| n_evaluator_episode=8, | |
| stop_value=6000, | |
| ), | |
| dataset=dict( | |
| env_type='mujoco', | |
| rtg_scale=1000, | |
| context_len=30, | |
| data_dir_prefix='d4rl/halfcheetah_medium_expert-v2.pkl', | |
| ), | |
| policy=dict( | |
| cuda=True, | |
| stop_value=6000, | |
| state_mean=None, | |
| state_std=None, | |
| evaluator_env_num=8, | |
| env_name='HalfCheetah-v3', | |
| rtg_target=6000, # max target return to go | |
| max_eval_ep_len=1000, # max lenght of one episode | |
| wt_decay=1e-4, | |
| warmup_steps=10000, | |
| context_len=20, | |
| weight_decay=0.1, | |
| clip_grad_norm_p=0.25, | |
| model=dict( | |
| state_dim=11, | |
| act_dim=3, | |
| n_blocks=3, | |
| h_dim=128, | |
| context_len=20, | |
| n_heads=1, | |
| drop_p=0.1, | |
| continuous=True, | |
| ), | |
| batch_size=64, | |
| learning_rate=1e-4, | |
| collect=dict( | |
| data_type='d4rl_trajectory', | |
| unroll_len=1, | |
| ), | |
| eval=dict(evaluator=dict(eval_freq=100, ), ), | |
| ), | |
| ) | |
| halfcheetah_dt_config = EasyDict(halfcheetah_dt_config) | |
| main_config = halfcheetah_dt_config | |
| halfcheetah_dt_create_config = dict( | |
| env=dict( | |
| type='mujoco', | |
| import_names=['dizoo.mujoco.envs.mujoco_env'], | |
| ), | |
| env_manager=dict(type='subprocess'), | |
| policy=dict(type='dt'), | |
| ) | |
| halfcheetah_dt_create_config = EasyDict(halfcheetah_dt_create_config) | |
| create_config = halfcheetah_dt_create_config | |
| if __name__ == "__main__": | |
| from ding.entry import serial_pipeline_dt | |
| config = deepcopy([main_config, create_config]) | |
| serial_pipeline_dt(config, seed=0, max_train_iter=1000) | |