code: def choose_action(self, curr_state):
      if not isinstance(self.spaces.action, DiscreteActionSpace):
          raise ValueError(
              "WolpertingerAgent works only for discrete control problems"
          )
  
      # convert to batch so we can run it through the network
      tf_input_state = self.prepare_batch_for_inference(
          curr_state, "actor"
      )
      actor_network = self.networks["actor"].online_network
      critic_network = self.networks["critic"].online_network
      proto_action = actor_network.predict(tf_input_state)
      proto_action = np.expand_dims(
          self.exploration_policy.get_action(proto_action), 0
      )
  
      nn_action_embeddings, indices, _, _ = self.knn_tree.query(
          keys=proto_action, k=self.ap.algorithm.k
      )
  
      # now move the actions through the critic and choose the one with the highest q value
      critic_inputs = copy.copy(tf_input_state)
      critic_inputs["observation"] = np.tile(
          critic_inputs["observation"], (self.ap.algorithm.k, 1)
      )
      critic_inputs["action"] = nn_action_embeddings[0]
      q_values = critic_network.predict(critic_inputs)[0]
      action = int(indices[0][np.argmax(q_values)])
      self.action_signal.add_sample(action)
      return ActionInfo(action=action, action_value=0)
  
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 4 for calling the WolpertingerAgent obj's underlying member method code with example usage: obj.choose_action(curr_state) and returns: ActionInfo