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 current annealing value
  
  # Returns
      Value to use in annealing
code: def get_current_value(self):
      """Return current annealing value
  
      # Returns
          Value to use in annealing
      """
      if self.agent.training:
          # Linear annealed: f(x) = ax + b.
          a = -float(self.value_max - self.value_min) / float(
              self.nb_steps
          )
          b = float(self.value_max)
          value = max(self.value_min, a * float(self.agent.step) + b)
      else:
          value = self.value_test
      return value
  
imports: from __future__ import division
  import numpy
  from rl.util import *
label_desc: Use image node_id 2 for calling the LinearAnnealedPolicy obj's underlying member method code with example usage: obj.get_current_value() and returns: value