text: from easydict import EasyDict
  
  walker2d_ddpg_gail_default_config = dict(
      exp_name='walker2d_ddpg_gail',
      env=dict(
          env_id='Walker2d-v3',
          norm_obs=dict(use_norm=False, ),
          norm_reward=dict(use_norm=False, ),
          collector_env_num=1,
          evaluator_env_num=8,
          use_act_scale=True,
          n_evaluator_episode=8,
          stop_value=6000,
      ),
      reward_model=dict(
          type='gail',
          input_size=23,
          hidden_size=256,
          batch_size=64,
          learning_rate=1e-3,
          update_per_collect=100,
          expert_data_path='walker2d_ddpg/expert_data_train.pkl',
          load_path='walker2d_ddpg_gail/reward_model/ckpt/ckpt_best.pth.tar',  # state_dict of the reward model
          collect_count=100000,
      ),
      policy=dict(
          load_path='walker2d_ddpg_gail/ckpt/ckpt_best.pth.tar',  # state_dict of the policy
          cuda=True,
          on_policy=False,
          random_collect_size=25000,
          model=dict(
              obs_shape=17,
              action_shape=6,
              twin_critic=False,
              actor_head_hidden_size=256,
              critic_head_hidden_size=256,
              actor_head_type='regression',
          ),
          learn=dict(
              update_per_collect=1,
              batch_size=256,
              learning_rate_actor=1e-3,
              learning_rate_critic=1e-3,
              ignore_done=False,
              target_theta=0.005,
              discount_factor=0.99,
              actor_update_freq=1,
              noise=False,
          ),
          collect=dict(
              n_sample=64,
              unroll_len=1,
              noise_sigma=0.1,
          ),
          other=dict(replay_buffer=dict(replay_buffer_size=1000000, ), ),
      )
  )
  walker2d_ddpg_gail_default_config = EasyDict(walker2d_ddpg_gail_default_config)
  main_config = walker2d_ddpg_gail_default_config
  
  walker2d_ddpg_gail_default_create_config = dict(
      env=dict(
          type='mujoco',
          import_names=['dizoo.mujoco.envs.mujoco_env'],
      ),
      env_manager=dict(type='base'),
      policy=dict(
          type='ddpg',
          import_names=['ding.policy.ddpg'],
      ),
      replay_buffer=dict(type='naive', ),
  )
  walker2d_ddpg_gail_default_create_config = EasyDict(walker2d_ddpg_gail_default_create_config)
  create_config = walker2d_ddpg_gail_default_create_config
  