'Use greedy sampling.'

def eval(self, sess):

    (initial_state, observations, actions, rewards, pads) = self._sample_episodes(sess, greedy=True)
    total_rewards = np.sum((np.array(rewards) * (1 - np.array(pads))), axis=0)
    return np.mean(total_rewards)
