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| from easydict import EasyDict | |
| from ding.entry import serial_pipeline_dyna | |
| # environment hypo | |
| env_id = 'Hopper-v2' | |
| obs_shape = 11 | |
| action_shape = 3 | |
| # gpu | |
| cuda = True | |
| main_config = dict( | |
| exp_name='hopper_sac_mbpo_seed0', | |
| env=dict( | |
| env_id=env_id, | |
| norm_obs=dict(use_norm=False, ), | |
| norm_reward=dict(use_norm=False, ), | |
| collector_env_num=1, | |
| evaluator_env_num=8, | |
| n_evaluator_episode=8, | |
| stop_value=100000, | |
| ), | |
| policy=dict( | |
| cuda=cuda, | |
| # it is better to put random_collect_size in policy.other | |
| random_collect_size=10000, | |
| model=dict( | |
| obs_shape=obs_shape, | |
| action_shape=action_shape, | |
| twin_critic=True, | |
| action_space='reparameterization', | |
| actor_head_hidden_size=256, | |
| critic_head_hidden_size=256, | |
| ), | |
| learn=dict( | |
| update_per_collect=20, | |
| batch_size=256, | |
| learning_rate_q=3e-4, | |
| learning_rate_policy=3e-4, | |
| learning_rate_alpha=3e-4, | |
| ignore_done=False, | |
| target_theta=0.005, | |
| discount_factor=0.99, | |
| alpha=0.2, | |
| reparameterization=True, | |
| auto_alpha=False, | |
| ), | |
| collect=dict( | |
| n_sample=1, | |
| unroll_len=1, | |
| ), | |
| command=dict(), | |
| eval=dict(evaluator=dict(eval_freq=500, )), # w.r.t envstep | |
| other=dict( | |
| # environment buffer | |
| replay_buffer=dict(replay_buffer_size=1000000, periodic_thruput_seconds=60), | |
| ), | |
| ), | |
| world_model=dict( | |
| eval_freq=250, # w.r.t envstep | |
| train_freq=250, # w.r.t envstep | |
| cuda=cuda, | |
| rollout_length_scheduler=dict( | |
| type='linear', | |
| rollout_start_step=20000, | |
| rollout_end_step=150000, | |
| rollout_length_min=1, | |
| rollout_length_max=15, | |
| ), | |
| model=dict( | |
| ensemble_size=7, | |
| elite_size=5, | |
| state_size=obs_shape, # has to be specified | |
| action_size=action_shape, # has to be specified | |
| reward_size=1, | |
| hidden_size=200, | |
| use_decay=True, | |
| batch_size=256, | |
| holdout_ratio=0.1, | |
| max_epochs_since_update=5, | |
| deterministic_rollout=True, | |
| ), | |
| other=dict( | |
| rollout_batch_size=100000, | |
| rollout_retain=4, | |
| real_ratio=0.05, | |
| imagination_buffer=dict(replay_buffer_size=6000000, ), | |
| ), | |
| ), | |
| ) | |
| main_config = EasyDict(main_config) | |
| create_config = dict( | |
| env=dict( | |
| type='mbmujoco', | |
| import_names=['dizoo.mujoco.envs.mujoco_env'], | |
| ), | |
| env_manager=dict(type='subprocess'), | |
| policy=dict( | |
| type='sac', | |
| import_names=['ding.policy.sac'], | |
| ), | |
| replay_buffer=dict(type='naive', ), | |
| imagination_buffer=dict(type='elastic', ), | |
| world_model=dict( | |
| type='mbpo', | |
| import_names=['ding.world_model.mbpo'], | |
| ), | |
| ) | |
| create_config = EasyDict(create_config) | |
| if __name__ == '__main__': | |
| serial_pipeline_dyna((main_config, create_config), seed=0, max_env_step=100000) | |