85209170887f87b0efb9477999a86d12562cc63f,ml/rl/test/environment/test_environment.py,TestEnvironment,test_gridworld_continuous_generate_samples,#TestEnvironment#,85

Before Change

        samples = env.generate_samples(
            num_samples, epsilon=1.0, discount_factor=0.9, multi_steps=num_steps
        )
        for i in range(num_samples):
            if samples.terminals[i][0]:
                break
            if i < num_samples - 1:
                self.assertEqual(samples.mdp_ids[i], samples.mdp_ids[i + 1])
                self.assertEqual(
                    samples.sequence_numbers[i] + 1, samples.sequence_numbers[i + 1]
                )
            for j in range(len(samples.terminals[i])):
                self.assertEqual(samples.rewards[i][j], samples.rewards[i + j][0])
                self.assertDictEqual(
                    samples.next_states[i][j], samples.next_states[i + j][0]
                )
                self.assertDictEqual(
                    samples.next_actions[i][j], samples.next_actions[i + j][0]
                )
                self.assertEqual(samples.terminals[i][j], samples.terminals[i + j][0])
                self.assertListEqual(
                    samples.possible_next_actions[i][j],
                    samples.possible_next_actions[i + j][0],
                )
                if samples.terminals[i][j]:
                    continue
                self.assertDictEqual(
                    samples.next_states[i][j], samples.states[i + j + 1]
                )
                self.assertDictEqual(
                    samples.next_actions[i][j], samples.actions[i + j + 1]
                )
                self.assertListEqual(
                    samples.possible_next_actions[i][j],
                    samples.possible_actions[i + j + 1],
                )

        single_step_samples = samples.to_single_step()
        for i in range(num_samples):
            if single_step_samples.terminals[i] is True:
                break
            self.assertEqual(single_step_samples.mdp_ids[i], samples.mdp_ids[i])
            self.assertEqual(
                single_step_samples.sequence_numbers[i], samples.sequence_numbers[i]
            )
            self.assertDictEqual(single_step_samples.states[i], samples.states[i])
            self.assertDictEqual(single_step_samples.actions[i], samples.actions[i])
            self.assertEqual(
                single_step_samples.action_probabilities[i],
                samples.action_probabilities[i],
            )
            self.assertEqual(single_step_samples.rewards[i], samples.rewards[i][0])
            self.assertListEqual(
                single_step_samples.possible_actions[i], samples.possible_actions[i]
            )
            self.assertDictEqual(
                single_step_samples.next_states[i], samples.next_states[i][0]
            )
            self.assertDictEqual(
                single_step_samples.next_actions[i], samples.next_actions[i][0]
            )
            self.assertEqual(single_step_samples.terminals[i], samples.terminals[i][0])
            self.assertListEqual(
                single_step_samples.possible_next_actions[i],
                samples.possible_next_actions[i][0],
            )

    def test_open_ai_gym_generate_samples_multi_step(self):
        env = OpenAIGymEnvironment(
            "CartPole-v0",
            epsilon=1.0,  // take random actions to collect training data

After Change

    def test_gridworld_continuous_generate_samples(self):
        env = GridworldContinuous()
        num_samples = 1000
        num_steps = 5
        samples = env.generate_samples(
            num_samples, epsilon=1.0, discount_factor=0.9, multi_steps=num_steps
        )
        self._check_samples(samples, num_samples, num_steps, True)

    def test_open_ai_gym_generate_samples_multi_step(self):
        env = OpenAIGymEnvironment(
            "CartPole-v0",

Instances