text: from gym.spaces import Space
  from typing import Optional
  
  from ray.rllib.utils.exploration.gaussian_noise import GaussianNoise
  from ray.rllib.utils.schedules import ConstantSchedule
  
  
  class PerWorkerGaussianNoise(GaussianNoise):
      """A per-worker Gaussian noise class for distributed algorithms.
  
      Sets the `scale` schedules of individual workers to a constant:
      0.4 ^ (1 + [worker-index] / float([num-workers] - 1) * 7)
      See Ape-X paper.
      """
  
      def __init__(self, action_space: Space, *, framework: Optional[str],
                   num_workers: Optional[int], worker_index: Optional[int],
                   **kwargs):
          """
          Args:
              action_space: The gym action space used by the environment.
              num_workers: The overall number of workers used.
              worker_index: The index of the Worker using this
                  Exploration.
              framework: One of None, "tf", "torch".
          """
          scale_schedule = None
          # Use a fixed, different epsilon per worker. See: Ape-X paper.
          if num_workers > 0:
              if worker_index > 0:
                  num_workers_minus_1 = float(num_workers - 1) \
                      if num_workers > 1 else 1.0
                  exponent = (1 + (worker_index / num_workers_minus_1) * 7)
                  scale_schedule = ConstantSchedule(
                      0.4**exponent, framework=framework)
              # Local worker should have zero exploration so that eval
              # rollouts run properly.
              else:
                  scale_schedule = ConstantSchedule(0.0, framework=framework)
  
          super().__init__(
              action_space,
              scale_schedule=scale_schedule,
              framework=framework,
              **kwargs)
  