class_docstr: Hook that will set a linearly interpolated value.
  
  You can use this hook to decay the learning rate by using a setter function
  as follows:
  
  .. code-block:: python
  
      def lr_setter(env, agent, value):
          agent.optimizer.lr = value
  
      hook = LinearInterpolationHook(10 ** 6, 1e-3, 0, lr_setter)
  
  
  Args:
      total_steps (int): Number of total steps.
      start_value (float): Start value.
      stop_value (float): Stop value.
      setter (callable): (env, agent, value) -> None
code: def __init__(self, total_steps, start_value, stop_value, setter):
      self.total_steps = total_steps
      self.start_value = start_value
      self.stop_value = stop_value
      self.setter = setter
  
file_path: pfrl/pfrl/experiments/hooks.py
imports: from abc import ABCMeta, abstractmethod
  import numpy
label_desc: Use image node_id 1 to create a new LinearInterpolationHook object from inherited base classes: StepHook with example: obj = LinearInterpolationHook(total_steps, start_value, stop_value, setter)