from easydict import EasyDict hopper_td3_data_generation_config = dict( exp_name='hopper_td3_data_generation_seed0', env=dict( env_id='Hopper-v3', 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=11000, ), policy=dict( cuda=True, random_collect_size=25000, model=dict( obs_shape=11, action_shape=3, twin_critic=True, actor_head_hidden_size=256, critic_head_hidden_size=256, action_space='regression', ), learn=dict( update_per_collect=1, batch_size=256, learning_rate_actor=1e-3, learning_rate_critic=1e-3, ignore_done=True, target_theta=0.005, discount_factor=0.99, actor_update_freq=2, noise=True, noise_sigma=0.2, noise_range=dict( min=-0.5, max=0.5, ), 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, noise_sigma=0.1, # 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', data_type='hdf5', ), other=dict(replay_buffer=dict(replay_buffer_size=1000000, ), ), ) ) hopper_td3_data_generation_config = EasyDict(hopper_td3_data_generation_config) main_config = hopper_td3_data_generation_config hopper_td3_data_generation_create_config = dict( env=dict( type='mujoco', import_names=['dizoo.mujoco.envs.mujoco_env'], ), env_manager=dict(type='subprocess'), policy=dict( type='td3', import_names=['ding.policy.td3'], ), replay_buffer=dict(type='naive', ), ) hopper_td3_data_generation_create_config = EasyDict(hopper_td3_data_generation_create_config) create_config = hopper_td3_data_generation_create_config