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"]
code: def __init__(
      self,
      initial_stddev=0.1,
      desired_action_stddev=0.1,
      adoption_coefficient=1.01,
  ):
      self.initial_stddev = initial_stddev
      self.desired_action_stddev = desired_action_stddev
      self.adoption_coefficient = adoption_coefficient
  
      self.current_stddev = initial_stddev
  
imports: from abc import ABC, abstractmethod
  import numpy
label_desc: Use image node_id 1 to create a new AdaptiveParamNoiseSpec object from inherited base classes: object with example: obj = AdaptiveParamNoiseSpec(initial_stddev, desired_action_stddev, adoption_coefficient)