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: return the standard deviation for the parameter noise
  
  :return: (dict) the stats of the noise
docstr_tok: ["return","the","standard","deviation","for","the","parameter","noise",":","return",":","(","dict",")","the","stats","of","the","noise"]
code: def get_stats(self):
      """
      return the standard deviation for the parameter noise
  
      :return: (dict) the stats of the noise
      """
      return {"param_noise_stddev": self.current_stddev}
  
imports: from abc import ABC, abstractmethod
  import numpy
label_desc: Use image node_id 3 for calling the AdaptiveParamNoiseSpec obj's underlying member method code with example usage: obj.get_stats() and returns: dict