code: def __init__(self, use_batchnorm=False):
      exploration_params = AdditiveNoiseParameters()
      exploration_params.noise_as_percentage_from_action_space = False
  
      super().__init__(
          algorithm=WolpertingerAlgorithmParameters(),
          exploration=exploration_params,
          memory=EpisodicExperienceReplayParameters(),
          networks=OrderedDict(
              [
                  (
                      "actor",
                      WolpertingerActorNetworkParameters(
                          use_batchnorm=use_batchnorm
                      ),
                  ),
                  (
                      "critic",
                      WolpertingerCriticNetworkParameters(
                          use_batchnorm=use_batchnorm
                      ),
                  ),
              ]
          ),
      )
  
imports: import copy
  from typing import Union
  from collections import OrderedDict
  import numpy
  from rl_coach.agents.ddpg_agent import DDPGAlgorithmParameters, DDPGActorNetworkParameters, DDPGCriticNetworkParameters, DDPGAgent
  from rl_coach.base_parameters import AgentParameters
  from rl_coach.core_types import ActionInfo
  from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
  from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
  from rl_coach.memories.non_episodic.differentiable_neural_dictionary import AnnoyDictionary
  from rl_coach.spaces import DiscreteActionSpace, BoxActionSpace
  from rl_coach.architectures.head_parameters import WolpertingerActorHeadParameters
label_desc: Use image node_id 1 to create a new WolpertingerAgentParameters object from inherited base classes: AgentParameters with example: obj = WolpertingerAgentParameters(use_batchnorm)