class_docstr: Normalizes the reward values based on Exponential Weighted Moving Average.   
docstr: :param alpha: the degree of weighting decrease, a constant smoothing factor between 0 and 1.
                A higher alpha discounts older observations faster
docstr_tok: [":","param","alpha",":","the","degree","of","weighting","decrease",",","a","constant","smoothing","factor","between","0","and","1",".","A","higher","alpha","discounts","older","observations","faster"]
code: def __init__(self, alpha: float = 0.01):
      """
      :param alpha: the degree of weighting decrease, a constant smoothing factor between 0 and 1.
                    A higher alpha discounts older observations faster
      """
      super().__init__()
      self.alpha = alpha
      self.moving_average = 0
      self.initialized = False
      self.checkpoint_file_extension = "ewma"
      self.supports_batching = True
  
file_path: coach/rl_coach/filters/reward/reward_ewma_normalization_filter.py
imports: import os
  import numpy
  import pickle
  from rl_coach.core_types import RewardType
  from rl_coach.filters.reward.reward_filter import RewardFilter
  from rl_coach.spaces import RewardSpace
  from rl_coach.utils import get_latest_checkpoint
label_desc: Use image node_id 1 to create a new RewardEwmaNormalizationFilter object from inherited base classes: RewardFilter with example: obj = RewardEwmaNormalizationFilter(alpha)