scripts/grad_estimators.py
def normalizer(self, array):
        """
        Returns a normalizing number, either the standard deviation of the array, or sigma.

        :param array: An array to calculate the standard deviation of. Redundant if using sigma scaling.
        """
        if self.norm_type == 'stdev':
            stdev = array.std()
            self.rewards_stdev.append(stdev)
            return stdev
        return self.sigma

        Returns a normalizing number, either the standard deviation of the array, or sigma.

        :param array: An array to calculate the standard deviation of. Redundant if using sigma scaling.
        
Returns a normalizing number, either the standard deviation of the array, or sigma.
Returns a normalizing number either the standard deviation of the array or sigma .
def normalizer(self, array):
        if self.norm_type == 'stdev':
            stdev = array.std()
            self.rewards_stdev.append(stdev)
            return stdev
        return self.sigma
def normalizer ( self , array ) : if self . norm_type == 'stdev' : stdev = array . std ( ) self . rewards_stdev . append ( stdev ) return stdev return self . sigma
Returns a normalizing number, either the standard deviation of the array, or sigma.
Returns a normalizing number either the standard deviation of the array or sigma .
"""
        Returns a normalizing number, either the standard deviation of the array, or sigma.

        :param array: An array to calculate the standard deviation of. Redundant if using sigma scaling.
        """
self
array
An array to calculate the standard deviation of. Redundant if using sigma scaling.
An array to calculate the standard deviation of . Redundant if using sigma scaling .