class_docstr: Actor-Critic Policy Gradient.
  
  See https://arxiv.org/pdf/1602.01783.pdf for a description of algorithm.
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 cntk
  import numpy
  import ast
  from .agent import AgentBaseClass
  from .shared.cntk_utils import negative_of_entropy_with_softmax
  from .shared.models import Models
  from .shared.policy_gradient_parameters import PolicyGradientParameters
label_desc: Use image node_id 7 for calling the ActorCritic obj's underlying member method code with example usage: obj._adjust_learning_rate() without return types