from easydict import EasyDict hopper_sac_data_generation_config = dict( exp_name='hopper_sac_data_generation_seed0', env=dict( env_id='Hopper-v3', norm_obs=dict(use_norm=False, ), norm_reward=dict(use_norm=False, ), collector_env_num=10, evaluator_env_num=8, n_evaluator_episode=8, stop_value=6000, ), policy=dict( cuda=True, random_collect_size=10000, model=dict( obs_shape=11, action_shape=3, twin_critic=True, action_space='reparameterization', actor_head_hidden_size=256, critic_head_hidden_size=256, ), learn=dict( update_per_collect=1, batch_size=256, learning_rate_q=1e-3, learning_rate_policy=1e-3, learning_rate_alpha=3e-4, ignore_done=False, target_theta=0.005, discount_factor=0.99, alpha=0.2, reparameterization=True, auto_alpha=False, learner=dict( # Model path should lead to a model. # Absolute path is recommended. # In DI-engine, it is ``exp_name/ckpt/ckpt_best.pth.tar``. load_path='model_path_placeholder', hook=dict( load_ckpt_before_run='model_path_placeholder', save_ckpt_after_run=False, ) ), ), collect=dict( n_sample=1, unroll_len=1, # Users should add their own data path here. Data path should lead to a file to store data or load the stored data. # Absolute path is recommended. # In DI-engine, it is usually located in ``exp_name`` directory save_path='data_path_placeholder', ), command=dict(), eval=dict(), other=dict(replay_buffer=dict(replay_buffer_size=1000000, ), ), ), ) hopper_sac_data_generation_config = EasyDict(hopper_sac_data_generation_config) main_config = hopper_sac_data_generation_config hopper_sac_data_genearation_create_config = dict( env=dict( type='mujoco', 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', ), ) hopper_sac_data_genearation_create_config = EasyDict(hopper_sac_data_genearation_create_config) create_config = hopper_sac_data_genearation_create_config