class TestExperienceReplayBuffer(unittest.TestCase):
def test_run(self):
np.random.seed(1)
random.seed(1)
torch.manual_seed(1)
self.replay_buffer = ExperienceReplayBuffer(5)
states = torch.arange(0, 20)
actions = torch.arange(0, 20).view((-1, 1))
rewards = torch.arange(0, 20)
expected_samples = torch.tensor([
[0, 0, 0],
[1, 1, 0],
[0, 1, 1],
[3, 0, 0],
[1, 4, 4],
[1, 2, 4],
[2, 4, 3],
[4, 7, 4],
[7, 4, 6],
[6, 5, 6],
])
expected_weights = np.ones((10, 3))
actual_samples = []
actual_weights = []
for i in range(10):
state = State(states[i])
next_state = State(states[i + 1], reward=rewards[i])
self.replay_buffer.store(state, actions[i], next_state)
sample = self.replay_buffer.sample(3)
actual_samples.append(sample[0].observation)
actual_weights.append(sample[-1])
tt.assert_equal(
torch.cat(actual_samples).view(expected_samples.shape),
expected_samples)
np.testing.assert_array_equal(expected_weights,
np.vstack(actual_weights))
def test_store_device(self):
if torch.cuda.is_available():
self.replay_buffer = ExperienceReplayBuffer(5,
device='cuda',
store_device='cpu')
states = torch.arange(0, 20).to('cuda')
actions = torch.arange(0, 20).view((-1, 1)).to('cuda')
rewards = torch.arange(0, 20).to('cuda')
state = State(states[0])
next_state = State(states[1], reward=rewards[1])
self.replay_buffer.store(state, actions[0], next_state)
sample = self.replay_buffer.sample(3)
self.assertEqual(sample[0].device, torch.device('cuda'))
self.assertEqual(self.replay_buffer.buffer[0][0].device,
torch.device('cpu'))
class TestExperienceReplayBuffer(unittest.TestCase):
def setUp(self):
np.random.seed(1)
random.seed(1)
torch.manual_seed(1)
self.replay_buffer = ExperienceReplayBuffer(5)
def test_run(self):
states = torch.arange(0, 20)
actions = torch.arange(0, 20)
rewards = torch.arange(0, 20)
expected_samples = torch.tensor([[0, 0, 0], [1, 1, 0], [0, 1, 1],
[3, 0, 0], [1, 4, 4], [1, 2, 4],
[2, 4, 3], [4, 7, 4], [7, 4, 6],
[6, 5, 6]])
expected_weights = np.ones((10, 3))
actual_samples = []
actual_weights = []
for i in range(10):
state = State(states[i].unsqueeze(0), torch.tensor([1]))
next_state = State(states[i + 1].unsqueeze(0), torch.tensor([1]))
self.replay_buffer.store(state, actions[i], rewards[i], next_state)
sample = self.replay_buffer.sample(3)
actual_samples.append(sample[0].features)
actual_weights.append(sample[-1])
tt.assert_equal(
torch.cat(actual_samples).view(expected_samples.shape),
expected_samples)
np.testing.assert_array_equal(expected_weights,
np.vstack(actual_weights))