class_docstr: Actor-Critic Policy Gradient.
  
  See https://arxiv.org/pdf/1602.01783.pdf for a description of algorithm.
code: def _update_networks(self):
      self._adjust_learning_rate()
  
      # Train the policy network on one minibatch.
      self._trainer.train_minibatch(
          {
              self._input_variables: np.array(
                  self._input_buffer
              ).astype(np.float32),
              self._policy_network_output_variables: np.array(
                  self._policy_network_output_buffer
              ).astype(np.float32),
              self._policy_network_weight_variables: np.array(
                  self._policy_network_weight_buffer
              ).astype(np.float32),
              self._value_network_output_variables: np.array(
                  self._value_network_output_buffer
              ).astype(np.float32),
          }
      )
  
      # Clear training data.
      self._input_buffer = []
      self._value_network_output_buffer = []
      self._policy_network_output_buffer = []
      self._policy_network_weight_buffer = []
  
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 13 for calling the ActorCritic obj's underlying member method code with example usage: obj._update_networks() without return types