test_action_value.py 文件源码

def test_compute_advantage(self):
        sample_actions = np.random.randint(self.action_size,
                                           size=self.batch_size)
        greedy_actions = self.q_values.argmax(axis=1)
        ret = self.qout.compute_advantage(sample_actions)
        self.assertIsInstance(ret, chainer.Variable)
        for b in range(self.batch_size):
            if sample_actions[b] == greedy_actions[b]:
                self.assertAlmostEqual(ret.data[b], 0)
            else:
                # An advantage to the optimal policy must be always negative
                self.assertLess(ret.data[b], 0)
                q = self.q_values[b, sample_actions[b]]
                v = self.q_values[b, greedy_actions[b]]
                adv = q - v
                self.assertAlmostEqual(ret.data[b], adv)