class_docstr: Implement the linear annealing policy
  
  Linear Annealing Policy computes a current threshold value and
  transfers it to an inner policy which chooses the action. The threshold
  value is following a linear function decreasing over time.
class_docstr_tok: ["Implement","the","linear","annealing","policy","Linear","Annealing","Policy","computes","a","current","threshold","value","and","transfers","it","to","an","inner","policy","which","chooses","the","action",".","The","threshold","value","is","following","a","linear","function","decreasing","over","time","."]
code: def __init__(
      self,
      inner_policy,
      attr,
      value_max,
      value_min,
      value_test,
      nb_steps,
  ):
      if not hasattr(inner_policy, attr):
          raise ValueError(
              'Policy does not have attribute "{}".'.format(attr)
          )
  
      super(LinearAnnealedPolicy, self).__init__()
  
      self.inner_policy = inner_policy
      self.attr = attr
      self.value_max = value_max
      self.value_min = value_min
      self.value_test = value_test
      self.nb_steps = nb_steps
  
imports: from __future__ import division
  import numpy
  from rl.util import *
label_desc: Use image node_id 1 to create a new LinearAnnealedPolicy object from inherited base classes: Policy with example: obj = LinearAnnealedPolicy(inner_policy, attr, value_max, value_min, value_test, nb_steps)