class_docstr: Implements adaptive parameter noise
  
  :param initial_stddev: (float) the initial value for the standard deviation of the noise
  :param desired_action_stddev: (float) the desired value for the standard deviation of the noise
  :param adoption_coefficient: (float) the update coefficient for the standard deviation of the noise
class_docstr_tok: ["Implements","adaptive","parameter","noise",":","param","initial_stddev",":","(","float",")","the","initial","value","for","the","standard","deviation","of","the","noise",":","param","desired_action_stddev",":","(","float",")","the","desired","value","for","the","standard","deviation","of","the","noise",":","param","adoption_coefficient",":","(","float",")","the","update","coefficient","for","the","standard","deviation","of","the","noise"]
docstr: update the standard deviation for the parameter noise
  
  :param distance: (float) the noise distance applied to the parameters
docstr_tok: ["update","the","standard","deviation","for","the","parameter","noise",":","param","distance",":","(","float",")","the","noise","distance","applied","to","the","parameters"]
code: def adapt(self, distance):
      """
      update the standard deviation for the parameter noise
  
      :param distance: (float) the noise distance applied to the parameters
      """
      if distance > self.desired_action_stddev:
          # Decrease stddev.
          self.current_stddev /= self.adoption_coefficient
      else:
          # Increase stddev.
          self.current_stddev *= self.adoption_coefficient
  
imports: from abc import ABC, abstractmethod
  import numpy
label_desc: Use image node_id 2 for calling the AdaptiveParamNoiseSpec obj's underlying member method code with example usage: obj.adapt(distance) without return types