class_docstr: Actor-Critic Policy Gradient.
  
  See https://arxiv.org/pdf/1602.01783.pdf for a description of algorithm.
code: def _discount_rewards(self, bootstrap_r):
      discounted_rewards = [0] * len(self._trajectory_rewards)
      r = bootstrap_r
      for t in reversed(range(len(self._trajectory_rewards))):
          r = r * self._parameters.gamma + self._trajectory_rewards[t]
          discounted_rewards[t] = r
      return discounted_rewards
  
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 14 for calling the ActorCritic obj's underlying member method code with example usage: obj._discount_rewards(bootstrap_r) and returns: discounted_rewards