class_docstr: Q-learning agent with tabular representation.
class_docstr_tok: ["Q-learning","agent","with","tabular","representation","."]
docstr: Last observed reward/state of the episode (which then terminates).
docstr_tok: ["Last","observed","reward\/state","of","the","episode","(","which","then","terminates",")","."]
code: def end(self, reward, next_state):
      """Last observed reward/state of the episode (which then terminates)."""
      self._adjust_learning_rate()
      self.step_count += 1
  
      td_err = reward - self._q[self._last_state, self._last_action]
      self._q[self._last_state, self._last_action] += self._eta * td_err
  
file_path: CNTK/bindings/python/cntk/contrib/deeprl/agent/tabular_qlearning.py
imports: import copy
  import numpy
  from .agent import AgentBaseClass
  from .shared.qlearning_parameters import QLearningParameters
label_desc: Use image node_id 4 for calling the TabularQLearning obj's underlying member method code with example usage: obj.end(reward, next_state) without return types