class_docstr: Exponentially decaying hyperparameter (specification key: `exponential`).
  
  Args:
      unit ("timesteps" | "episodes" | "updates"): Unit of decay schedule
          (<span style="color:#C00000"><b>required</b></span>).
      num_steps (int): Number of decay steps
          (<span style="color:#C00000"><b>required</b></span>).
      initial_value (float): Initial value
          (<span style="color:#C00000"><b>required</b></span>).
      decay_rate (float): Decay rate
          (<span style="color:#C00000"><b>required</b></span>).
      staircase (bool): Whether to apply decay in a discrete staircase, as opposed to continuous,
          fashion (<span style="color:#00C000"><b>default</b></span>: false).
      name (string): <span style="color:#0000C0"><b>internal use</b></span>.
      dtype (type): <span style="color:#0000C0"><b>internal use</b></span>.
      min_value (dtype-compatible value): <span style="color:#0000C0"><b>internal use</b></span>.
      max_value (dtype-compatible value): <span style="color:#0000C0"><b>internal use</b></span>.
code: def __init__(
      self,
      *,
      unit,
      num_steps,
      initial_value,
      decay_rate,
      staircase=False,
      name=None,
      dtype=None,
      min_value=None,
      max_value=None,
      **kwargs,
  ):
      super().__init__(
          decay="exponential",
          unit=unit,
          num_steps=num_steps,
          initial_value=initial_value,
          name=name,
          dtype=dtype,
          min_value=min_value,
          max_value=max_value,
          decay_rate=decay_rate,
          staircase=staircase,
          **kwargs,
      )
  
imports: import tensorflow
  from tensorforce import TensorforceError
  from tensorforce.core import tf_util
  from tensorforce.core.parameters import Decaying
label_desc: Use image node_id 1 to create a new Exponential object from inherited base classes: Decaying with example: obj = Exponential()