class_docstr: Q-learning agent.
  
  Including:
  - DQN https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf
  - Prioritized Experience Replay https://arxiv.org/pdf/1511.05952.pdf
  - Dueling Network https://arxiv.org/pdf/1511.06581.pdf
  - Double Q Learning https://arxiv.org/pdf/1509.06461.pdf
class_docstr_tok: ["Q-learning","agent",".","Including",":","-","DQN","https",":","\/\/storage.googleapis.com\/deepmind-media\/dqn\/DQNNaturePaper.pdf","-","Prioritized","Experience","Replay","https",":","\/\/arxiv.org\/pdf\/1511.05952.pdf","-","Dueling","Network","https",":","\/\/arxiv.org\/pdf\/1511.06581.pdf","-","Double","Q","Learning","https",":","\/\/arxiv.org\/pdf\/1509.06461.pdf"]
code: def _adjust_learning_rate(self):
      if self._parameters.initial_eta != self._parameters.eta_minimum:
          eta = self._parameters.eta_minimum + max(
              0,
              (
                  self._parameters.initial_eta
                  - self._parameters.eta_minimum
              )
              * (
                  1
                  - float(self.step_count)
                  / self._parameters.eta_decay_step_count
              ),
          )
  
          self._trainer.parameter_learners[0].reset_learning_rate(
              C.learners.learning_parameter_schedule_per_sample(eta)
          )
  
imports: import math
  import cntk
  import numpy
  import ast
  from .agent import AgentBaseClass
  from .shared.cntk_utils import huber_loss
  from .shared.models import Models
  from .shared.qlearning_parameters import QLearningParameters
  from .shared.replay_memory import ReplayMemory
label_desc: Use image node_id 7 for calling the QLearning obj's underlying member method code with example usage: obj._adjust_learning_rate() without return types