class TimeFeatureTest(unittest.TestCase):
def setUp(self):
torch.manual_seed(2)
self.test_agent = TestAgent()
self.agent = TimeFeature(self.test_agent)
def test_init(self):
state = State(torch.randn(4))
self.agent.act(state)
tt.assert_allclose(self.test_agent.last_state.observation, torch.tensor(
[0.3923, -0.2236, -0.3195, -1.2050, 0.0000]), atol=1e-04)
def test_single_env(self):
state = State(torch.randn(4))
self.agent.act(state)
tt.assert_allclose(self.test_agent.last_state.observation, torch.tensor(
[0.3923, -0.2236, -0.3195, -1.2050, 0.]), atol=1e-04)
self.agent.act(state)
tt.assert_allclose(self.test_agent.last_state.observation, torch.tensor(
[0.3923, -0.2236, -0.3195, -1.2050, 1e-3]), atol=1e-04)
self.agent.act(state)
tt.assert_allclose(self.test_agent.last_state.observation, torch.tensor(
[0.3923, -0.2236, -0.3195, -1.2050, 2e-3]), atol=1e-04)
def test_reset(self):
state = State(torch.randn(4))
self.agent.act(state)
tt.assert_allclose(self.test_agent.last_state.observation, torch.tensor(
[0.3923, -0.2236, -0.3195, -1.2050, 0.0000]), atol=1e-04)
self.agent.act(state)
tt.assert_allclose(self.test_agent.last_state.observation, torch.tensor(
[0.3923, -0.2236, -0.3195, -1.2050, 1e-3]), atol=1e-04)
self.agent.act(State(state.observation, done=True))
tt.assert_allclose(self.test_agent.last_state.observation, torch.tensor(
[0.3923, -0.2236, -0.3195, -1.2050, 2e-3]), atol=1e-04)
self.agent.act(State(state.observation))
tt.assert_allclose(self.test_agent.last_state.observation, torch.tensor(
[0.3923, -0.2236, -0.3195, -1.2050, 0.0000]), atol=1e-04)
self.agent.act(state)
tt.assert_allclose(self.test_agent.last_state.observation, torch.tensor(
[0.3923, -0.2236, -0.3195, -1.2050, 1e-3]), atol=1e-04)
def test_multi_env(self):
state = StateArray(torch.randn(2, 2), (2,))
self.agent.act(state)
tt.assert_allclose(self.test_agent.last_state.observation, torch.tensor(
[[0.3923, -0.2236, 0.], [-0.3195, -1.2050, 0.]]), atol=1e-04)
self.agent.act(state)
tt.assert_allclose(self.test_agent.last_state.observation, torch.tensor(
[[0.3923, -0.2236, 1e-3], [-0.3195, -1.2050, 1e-3]]), atol=1e-04)
self.agent.act(StateArray(state.observation, (2,), done=torch.tensor([False, True])))
tt.assert_allclose(self.test_agent.last_state.observation, torch.tensor(
[[0.3923, -0.2236, 2e-3], [-0.3195, -1.2050, 2e-3]]), atol=1e-04)
self.agent.act(state)
tt.assert_allclose(self.test_agent.last_state.observation, torch.tensor(
[[0.3923, -0.2236, 3e-3], [-0.3195, -1.2050, 0.]]), atol=1e-04)
self.agent.act(state)
tt.assert_allclose(self.test_agent.last_state.observation, torch.tensor(
[[0.3923, -0.2236, 4e-3], [-0.3195, -1.2050, 1e-3]]), atol=1e-04)
class TimeFeatureTest(unittest.TestCase):
def setUp(self):
torch.manual_seed(2)
self.test_agent = TestAgent()
self.agent = TimeFeature(self.test_agent)
def test_init(self):
state = State(torch.randn(1, 4))
self.agent.act(state, 0)
tt.assert_allclose(self.test_agent.last_state.features,
torch.tensor(
[[0.3923, -0.2236, -0.3195, -1.2050, 0.0000]]),
atol=1e-04)
def test_single_env(self):
state = State(torch.randn(1, 4))
self.agent.act(state, 0)
tt.assert_allclose(self.test_agent.last_state.features,
torch.tensor(
[[0.3923, -0.2236, -0.3195, -1.2050, 0.]]),
atol=1e-04)
self.agent.act(state, 0)
tt.assert_allclose(self.test_agent.last_state.features,
torch.tensor(
[[0.3923, -0.2236, -0.3195, -1.2050, 1e-3]]),
atol=1e-04)
self.agent.act(state, 0)
tt.assert_allclose(self.test_agent.last_state.features,
torch.tensor(
[[0.3923, -0.2236, -0.3195, -1.2050, 2e-3]]),
atol=1e-04)
def test_reset(self):
state = State(torch.randn(1, 4))
self.agent.act(state, 0)
tt.assert_allclose(self.test_agent.last_state.features,
torch.tensor(
[[0.3923, -0.2236, -0.3195, -1.2050, 0.0000]]),
atol=1e-04)
self.agent.act(state, 0)
tt.assert_allclose(self.test_agent.last_state.features,
torch.tensor(
[[0.3923, -0.2236, -0.3195, -1.2050, 1e-3]]),
atol=1e-04)
self.agent.act(State(state.features, DONE), 0)
tt.assert_allclose(self.test_agent.last_state.features,
torch.tensor(
[[0.3923, -0.2236, -0.3195, -1.2050, 2e-3]]),
atol=1e-04)
self.agent.act(State(state.features), 0)
tt.assert_allclose(self.test_agent.last_state.features,
torch.tensor(
[[0.3923, -0.2236, -0.3195, -1.2050, 0.0000]]),
atol=1e-04)
self.agent.act(state, 0)
tt.assert_allclose(self.test_agent.last_state.features,
torch.tensor(
[[0.3923, -0.2236, -0.3195, -1.2050, 1e-3]]),
atol=1e-04)
def test_multi_env(self):
state = State(torch.randn(2, 2))
self.agent.act(state, 0)
tt.assert_allclose(self.test_agent.last_state.features,
torch.tensor([[0.3923, -0.2236, 0.],
[-0.3195, -1.2050, 0.]]),
atol=1e-04)
self.agent.act(state, 0)
tt.assert_allclose(self.test_agent.last_state.features,
torch.tensor([[0.3923, -0.2236, 1e-3],
[-0.3195, -1.2050, 1e-3]]),
atol=1e-04)
self.agent.act(State(state.features, torch.tensor([1., 0.])), 0)
tt.assert_allclose(self.test_agent.last_state.features,
torch.tensor([[0.3923, -0.2236, 2e-3],
[-0.3195, -1.2050, 2e-3]]),
atol=1e-04)
self.agent.act(state, 0)
tt.assert_allclose(self.test_agent.last_state.features,
torch.tensor([[0.3923, -0.2236, 3e-3],
[-0.3195, -1.2050, 0.]]),
atol=1e-04)
self.agent.act(state, 0)
tt.assert_allclose(self.test_agent.last_state.features,
torch.tensor([[0.3923, -0.2236, 4e-3],
[-0.3195, -1.2050, 1e-3]]),
atol=1e-04)