class_docstr: Actor-Critic Policy Gradient.
  
  See https://arxiv.org/pdf/1602.01783.pdf for a description of algorithm.
docstr: Choose an action according to policy.
  
  Args:
      state (object): observation seen by agent, which can be different
          from what is provided by the environment. The difference comes
          from preprcessing.
  
  Returns:
      action (int): action choosen by agent.
      debug_info (object): probability vector the action is sampled from.
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code: def _choose_action(self, state):
      """
      Choose an action according to policy.
  
      Args:
          state (object): observation seen by agent, which can be different
              from what is provided by the environment. The difference comes
              from preprcessing.
  
      Returns:
          action (int): action choosen by agent.
          debug_info (object): probability vector the action is sampled from.
      """
      action_probs = C.ops.softmax(
          self._evaluate_model(self._policy_network, state)
      ).eval()
      return (
          np.random.choice(self._num_actions, p=action_probs),
          action_probs,
      )
  
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 8 for calling the ActorCritic obj's underlying member method code with example usage: obj._choose_action(state) and returns: np,  action_probs