Fixed failing test case

    policy_layers = self._policy.create_layers(features)

    action_prob = policy_layers['action_prob']

    value = policy_layers['value']

    rewards = Weights(shape=(None, 1))

    advantages = Weights(shape=(None, 1))

    rewards = Weights(shape=(None,))

    advantages = Weights(shape=(None,))

    actions = Label(shape=(None, self._env.n_actions))

    loss = A3CLoss(

        self.value_weight,

      action = np.random.choice(np.arange(n_actions), p=probabilities[0])

      actions.append(np.zeros(n_actions))

      actions[i][action] = 1.0

      values.append(value[0])

      values.append(float(value))

      rewards.append(self.env.step(action))

    if not self.env.terminated:

      # Add an estimate of the reward for the rest of the episode.

      feed_dict = _create_feed_dict(self.features, self.env.state)

      rewards[-1] += self.a3c.discount_factor * session.run(

          self.value.out_tensor, feed_dict)

      rewards[-1] += self.a3c.discount_factor * float(

          session.run(self.value.out_tensor, feed_dict))

    for j in range(len(rewards) - 1, 0, -1):

      rewards[j - 1] += self.a3c.discount_factor * rewards[j]

    rewards_array = np.array(rewards).reshape((len(rewards), 1))

    rewards_array = np.array(rewards)

    advantages = rewards_array - np.array(values)

    if self.env.terminated:

      self.env.reset()