class_docstr: Bootstrapped exploration policy is currently only used for discrete action spaces along with the
  Bootstrapped DQN agent. It assumes that there is an ensemble of network heads, where each one predicts the
  values for all the possible actions. For each episode, a single head is selected to lead the agent, according
  to its value predictions. In evaluation, the action is selected using a majority vote over all the heads
  predictions.
  
  .. note::
     This exploration policy will only work for Discrete action spaces with Bootstrapped DQN style agents,
     since it requires the agent to have a network with multiple heads.
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code: def get_control_param(self):
      return self.selected_head
  
file_path: coach/rl_coach/exploration_policies/bootstrapped.py
imports: from typing import List
  import numpy
  from rl_coach.core_types import RunPhase, ActionType
  from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
  from rl_coach.exploration_policies.e_greedy import EGreedy, EGreedyParameters
  from rl_coach.exploration_policies.exploration_policy import ExplorationParameters
  from rl_coach.schedules import Schedule, LinearSchedule
  from rl_coach.spaces import ActionSpace
label_desc: Use image node_id 4 for calling the Bootstrapped obj's underlying member method code with example usage: obj.get_control_param() and returns: self