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)
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_from_list(self):
state1 = State(torch.randn(1, 4), mask=DONE, info=['a'])
state2 = State(torch.randn(1, 4), mask=NOT_DONE, info=['b'])
state3 = State(torch.randn(1, 4))
state = State.from_list([state1, state2, state3])
tt.assert_equal(state.raw,
torch.cat((state1.raw, state2.raw, state3.raw)))
tt.assert_equal(state.mask, torch.tensor([0, 1, 1]))
self.assertEqual(state.info, ['a', 'b', None])
def test_constructor_defaults(self):
raw = torch.randn(3, 4)
state = State(raw)
tt.assert_equal(state.features, raw)
tt.assert_equal(state.mask, torch.ones(3))
tt.assert_equal(state.raw, raw)
self.assertEqual(state.info, [None] * 3)
def test_get_item(self):
raw = torch.randn(3, 4)
states = State(raw)
state = states[2]
tt.assert_equal(state.raw, raw[2].unsqueeze(0))
tt.assert_equal(state.mask, NOT_DONE)
self.assertEqual(state.info, [None])
def test_from_gym(self):
gym_obs = np.array([1, 2, 3])
done = True
info = 'a'
state = State.from_gym(gym_obs, done, info)
tt.assert_equal(state.raw, torch.tensor([[1, 2, 3]]))
tt.assert_equal(state.mask, DONE)
self.assertEqual(state.info, ['a'])
def test_custom_constructor_args(self):
raw = torch.randn(3, 4)
mask = torch.zeros(3)
info = ['a', 'b', 'c']
state = State(raw, mask=mask, info=info)
tt.assert_equal(state.features, raw)
tt.assert_equal(state.mask, torch.zeros(3))
self.assertEqual(state.info, info)
def test_len(self):
state = State(torch.randn(3, 4))
self.assertEqual(len(state), 3)
def test_done(self):
raw = torch.randn(1, 4)
state = State(raw, mask=DONE)
self.assertTrue(state.done)
def test_not_done(self):
state = State(torch.randn(1, 4))
self.assertFalse(state.done)