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","."]
docstr: Return configurations of LinearAnnealedPolicy
  
  # Returns
      Dict of config
code: def get_config(self):
      """Return configurations of LinearAnnealedPolicy
  
      # Returns
          Dict of config
      """
      config = super(LinearAnnealedPolicy, self).get_config()
      config["attr"] = self.attr
      config["value_max"] = self.value_max
      config["value_min"] = self.value_min
      config["value_test"] = self.value_test
      config["nb_steps"] = self.nb_steps
      config["inner_policy"] = get_object_config(self.inner_policy)
      return config
  
imports: from __future__ import division
  import numpy
  from rl.util import *
label_desc: Use image node_id 6 for calling the LinearAnnealedPolicy obj's underlying member method code with example usage: obj.get_config() and returns: config