class_docstr: Rescales the reward by a given factor. Rescaling the rewards of the environment has been
  observed to have a large effect (negative or positive) on the behavior of the learning process.
docstr: :param rescale_factor: The reward rescaling factor by which the reward will be multiplied
docstr_tok: [":","param","rescale_factor",":","The","reward","rescaling","factor","by","which","the","reward","will","be","multiplied"]
code: def __init__(self, rescale_factor: float):
      """
      :param rescale_factor: The reward rescaling factor by which the reward will be multiplied
      """
      super().__init__()
      self.rescale_factor = rescale_factor
  
      if rescale_factor == 0:
          raise ValueError(
              "The reward rescale value can not be set to 0"
          )
  
file_path: coach/rl_coach/filters/reward/reward_rescale_filter.py
imports: 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 RewardRescaleFilter object from inherited base classes: RewardFilter with example: obj = RewardRescaleFilter(rescale_factor)