class_docstr: Base class for defining an agent.
docstr: Last observed reward/state of the episode (which then terminates).
  
  Args:
      reward (float) : amount of reward returned after previous action.
      next_state (object): observation provided by the environment.
docstr_tok: ["Last","observed","reward\/state","of","the","episode","(","which","then","terminates",")",".","Args",":","reward","(","float",")",":","amount","of","reward","returned","after","previous","action",".","next_state","(","object",")",":","observation","provided","by","the","environment","."]
code: def end(self, reward, next_state):
      """
      Last observed reward/state of the episode (which then terminates).
  
      Args:
          reward (float) : amount of reward returned after previous action.
          next_state (object): observation provided by the environment.
      """
      pass
  
imports: from abc import ABCMeta, abstractmethod
  import numpy
  from importlib import import_module
  from .shared.discretize import BoxSpaceDiscretizer
label_desc: Use image node_id 4 for calling the AgentBaseClass obj's underlying member method code with example usage: obj.end(reward, next_state) without return types