exp_config = {
    'main_config': {
        'exp_name': 'Pendulum-v1-EfficientZero',
        'seed': 0,
        'env': {
            'env_id': 'Pendulum-v1',
            'continuous': False,
            'manually_discretization': True,
            'each_dim_disc_size': 11,
            'collector_env_num': 8,
            'evaluator_env_num': 3,
            'n_evaluator_episode': 3,
            'manager': {
                'shared_memory': False
            }
        },
        'policy': {
            'on_policy': False,
            'cuda': True,
            'multi_gpu': False,
            'bp_update_sync': True,
            'traj_len_inf': False,
            'model': {
                'observation_shape': 3,
                'action_space_size': 11,
                'model_type': 'mlp',
                'lstm_hidden_size': 128,
                'latent_state_dim': 128
            },
            'use_rnd_model': False,
            'sampled_algo': False,
            'gumbel_algo': False,
            'mcts_ctree': True,
            'collector_env_num': 8,
            'evaluator_env_num': 3,
            'env_type': 'not_board_games',
            'action_type': 'fixed_action_space',
            'battle_mode': 'play_with_bot_mode',
            'monitor_extra_statistics': True,
            'game_segment_length': 50,
            'transform2string': False,
            'gray_scale': False,
            'use_augmentation': False,
            'augmentation': ['shift', 'intensity'],
            'ignore_done': False,
            'update_per_collect': 200,
            'model_update_ratio': 0.1,
            'batch_size': 256,
            'optim_type': 'Adam',
            'learning_rate': 0.003,
            'target_update_freq': 100,
            'target_update_freq_for_intrinsic_reward': 1000,
            'weight_decay': 0.0001,
            'momentum': 0.9,
            'grad_clip_value': 10,
            'n_episode': 8,
            'num_simulations': 50,
            'discount_factor': 0.997,
            'td_steps': 5,
            'num_unroll_steps': 5,
            'reward_loss_weight': 1,
            'value_loss_weight': 0.25,
            'policy_loss_weight': 1,
            'policy_entropy_loss_weight': 0,
            'ssl_loss_weight': 2,
            'lr_piecewise_constant_decay': False,
            'threshold_training_steps_for_final_lr': 50000,
            'manual_temperature_decay': False,
            'threshold_training_steps_for_final_temperature': 100000,
            'fixed_temperature_value': 0.25,
            'use_ture_chance_label_in_chance_encoder': False,
            'use_priority': True,
            'priority_prob_alpha': 0.6,
            'priority_prob_beta': 0.4,
            'root_dirichlet_alpha': 0.3,
            'root_noise_weight': 0.25,
            'random_collect_episode_num': 0,
            'eps': {
                'eps_greedy_exploration_in_collect': False,
                'type': 'linear',
                'start': 1.0,
                'end': 0.05,
                'decay': 100000
            },
            'cfg_type': 'EfficientZeroPolicyDict',
            'lstm_horizon_len': 5,
            'reanalyze_ratio': 0,
            'eval_freq': 2000,
            'replay_buffer_size': 1000000
        },
        'wandb_logger': {
            'gradient_logger': False,
            'video_logger': False,
            'plot_logger': False,
            'action_logger': False,
            'return_logger': False
        }
    },
    'create_config': {
        'env': {
            'type':
            'pendulum_lightzero',
            'import_names':
            ['zoo.classic_control.pendulum.envs.pendulum_lightzero_env']
        },
        'env_manager': {
            'type': 'subprocess'
        },
        'policy': {
            'type': 'efficientzero',
            'import_names': ['lzero.policy.efficientzero']
        }
    }
}