class_docstr: Implements Boltzmann-Gumbel exploration (BGE) adapted for Q learning
  based on the paper Boltzmann Exploration Done Right
  (https://arxiv.org/pdf/1705.10257.pdf).
  
  BGE is invariant with respect to the mean of the rewards but not their
  variance. The parameter C, which defaults to 1, can be used to correct for
  this, and should be set to the least upper bound on the standard deviation
  of the rewards.
  
  BGE is only available for training, not testing. For testing purposes, you
  can achieve approximately the same result as BGE after training for N steps
  on K actions with parameter C by using the BoltzmannQPolicy and setting
  tau = C/sqrt(N/K).
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docstr: Return configurations of BoltzmannGumbelQPolicy
  
  # Returns
      Dict of config
code: def get_config(self):
      """Return configurations of BoltzmannGumbelQPolicy
  
      # Returns
          Dict of config
      """
      config = super(BoltzmannGumbelQPolicy, self).get_config()
      config["C"] = self.C
      return config
  
imports: from __future__ import division
  import numpy
  from rl.util import *
label_desc: Use image node_id 3 for calling the BoltzmannGumbelQPolicy obj's underlying member method code with example usage: obj.get_config() and returns: config