class_docstr: Clips the reward values into a given range. For example, in DQN, the Atari rewards are
  clipped into the range -1 and 1 in order to control the scale of the returns.
docstr: :param clipping_low: The low threshold for reward clipping
  :param clipping_high: The high threshold for reward clipping
docstr_tok: [":","param","clipping_low",":","The","low","threshold","for","reward","clipping",":","param","clipping_high",":","The","high","threshold","for","reward","clipping"]
code: def __init__(
      self, clipping_low: float = -np.inf, clipping_high: float = np.inf
  ):
      """
      :param clipping_low: The low threshold for reward clipping
      :param clipping_high: The high threshold for reward clipping
      """
      super().__init__()
      self.clipping_low = clipping_low
      self.clipping_high = clipping_high
  
      if clipping_low > clipping_high:
          raise ValueError(
              "The reward clipping low must be lower than the reward clipping max"
          )
  
file_path: coach/rl_coach/filters/reward/reward_clipping_filter.py
imports: import numpy
  from rl_coach.core_types import RewardType
  from rl_coach.filters.reward.reward_filter import RewardFilter
  from rl_coach.spaces import RewardSpace
label_desc: Use image node_id 1 to create a new RewardClippingFilter object from inherited base classes: RewardFilter with example: obj = RewardClippingFilter(clipping_low, clipping_high)