class_docstr: Normalizes the reward values with a running mean and standard deviation of
  all the rewards seen so far. When working with multiple workers, the statistics used for the normalization operation
  are accumulated over all the workers.
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docstr: :param clip_min: The minimum value to allow after normalizing the reward
  :param clip_max: The maximum value to allow after normalizing the reward
docstr_tok: [":","param","clip_min",":","The","minimum","value","to","allow","after","normalizing","the","reward",":","param","clip_max",":","The","maximum","value","to","allow","after","normalizing","the","reward"]
code: def __init__(self, clip_min: float = -5.0, clip_max: float = 5.0):
      """
      :param clip_min: The minimum value to allow after normalizing the reward
      :param clip_max: The maximum value to allow after normalizing the reward
      """
      super().__init__()
      self.clip_min = clip_min
      self.clip_max = clip_max
      self.running_rewards_stats = None
  
file_path: coach/rl_coach/filters/reward/reward_normalization_filter.py
imports: import os
  import numpy
  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.utilities.shared_running_stats import NumpySharedRunningStats
label_desc: Use image node_id 1 to create a new RewardNormalizationFilter object from inherited base classes: RewardFilter with example: obj = RewardNormalizationFilter(clip_min, clip_max)