code: def choose_action(self, curr_state):
      if self.exploration_policy.requires_action_values():
          # predict the future measurements
          tf_input_state = self.prepare_batch_for_inference(
              curr_state, "main"
          )
          tf_input_state["goal"] = np.expand_dims(self.current_goal, 0)
          measurements_future_prediction = self.networks[
              "main"
          ].online_network.predict(tf_input_state)[0]
          action_values = np.zeros(len(self.spaces.action.actions))
          num_steps_used_for_objective = len(
              self.ap.algorithm.future_measurements_weights
          )
  
          # calculate the score of each action by multiplying it's future measurements with the goal vector
          for action_idx in range(len(self.spaces.action.actions)):
              action_measurements = measurements_future_prediction[
                  action_idx
              ]
              action_measurements = np.reshape(
                  action_measurements,
                  (
                      self.ap.algorithm.num_predicted_steps_ahead,
                      self.spaces.state["measurements"].shape[0],
                  ),
              )
              future_steps_values = np.dot(
                  action_measurements, self.current_goal
              )
              action_values[action_idx] = np.dot(
                  future_steps_values[-num_steps_used_for_objective:],
                  self.ap.algorithm.future_measurements_weights,
              )
      else:
          action_values = None
  
      # choose action according to the exploration policy and the current phase (evaluating or training the agent)
      action, _ = self.exploration_policy.get_action(action_values)
  
      if action_values is not None:
          action_values = action_values.squeeze()
          action_info = ActionInfo(
              action=action, action_value=action_values[action]
          )
      else:
          action_info = ActionInfo(action=action)
  
      return action_info
  
imports: import copy
  from enum import Enum
  from typing import Union
  import numpy
  from rl_coach.agents.agent import Agent
  from rl_coach.architectures.head_parameters import MeasurementsPredictionHeadParameters
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.architectures.tensorflow_components.layers import Conv2d, Dense
  from rl_coach.base_parameters import AlgorithmParameters, AgentParameters, NetworkParameters, MiddlewareScheme
  from rl_coach.core_types import ActionInfo, EnvironmentSteps, RunPhase
  from rl_coach.exploration_policies.e_greedy import EGreedyParameters
  from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
  from rl_coach.memories.memory import MemoryGranularity
  from rl_coach.spaces import SpacesDefinition, VectorObservationSpace
label_desc: Use image node_id 4 for calling the DFPAgent obj's underlying member method code with example usage: obj.choose_action(curr_state) and returns: action_info