class_docstr: A combination of the eps-greedy and Boltzman q-policy.
  
  Wiering, M.: Explorations in Efficient Reinforcement Learning.
  PhD thesis, University of Amsterdam, Amsterdam (1999)
  
  https://pure.uva.nl/ws/files/3153478/8461_UBA003000033.pdf
class_docstr_tok: ["A","combination","of","the","eps-greedy","and","Boltzman","q-policy",".","Wiering",",","M.",":","Explorations","in","Efficient","Reinforcement","Learning",".","PhD","thesis",",","University","of","Amsterdam",",","Amsterdam","(","1999",")","https",":","\/\/pure.uva.nl\/ws\/files\/3153478\/8461_UBA003000033.pdf"]
code: def __init__(self, eps=0.1, tau=1.0, clip=(-500.0, 500.0)):
      super(MaxBoltzmannQPolicy, self).__init__()
      self.eps = eps
      self.tau = tau
      self.clip = clip
  
imports: from __future__ import division
  import numpy
  from rl.util import *
label_desc: Use image node_id 1 to create a new MaxBoltzmannQPolicy object from inherited base classes: Policy with example: obj = MaxBoltzmannQPolicy(eps, tau, clip)