class TestValueAgent(TestCase):
def setUp(self) -> None:
self.env = gym.make("CartPole-v0")
self.net = Mock(return_value=torch.Tensor([[0.0, 100.0]]))
self.state = torch.tensor(self.env.reset())
self.device = self.state.device
self.value_agent = ValueAgent(self.net, self.env.action_space.n)
def test_value_agent(self):
action = self.value_agent(self.state, self.device)
self.assertIsInstance(action, int)
def test_value_agent_GET_ACTION(self):
action = self.value_agent.get_action(self.state, self.device)
self.assertIsInstance(action, int)
self.assertEqual(action, 1)
def test_value_agent_RANDOM(self):
action = self.value_agent.get_random_action()
self.assertIsInstance(action, int)
class TestValueAgent(TestCase):
def setUp(self) -> None:
self.env = gym.make("CartPole-v0")
self.net = Mock(return_value=Tensor([[0.0, 100.0]]))
self.state = [self.env.reset()]
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.value_agent = ValueAgent(self.net, self.env.action_space.n)
def test_value_agent(self):
action = self.value_agent(self.state, self.device)
self.assertIsInstance(action, list)
self.assertIsInstance(action[0], int)
def test_value_agent_get_action(self):
action = self.value_agent.get_action(self.state, self.device)
self.assertIsInstance(action, np.ndarray)
self.assertEqual(action[0], 1)
def test_value_agent_random(self):
action = self.value_agent.get_random_action(self.state)
self.assertIsInstance(action[0], int)