def test_max_bounded(self):
n_batch = 20
ndim_action = 3
mu = np.random.randn(n_batch, ndim_action).astype(np.float32)
mat = np.broadcast_to(
np.eye(ndim_action, dtype=np.float32)[None],
(n_batch, ndim_action, ndim_action),
)
v = np.random.randn(n_batch).astype(np.float32)
min_action, max_action = -1.3, 1.3
q_out = action_value.QuadraticActionValue(torch.tensor(mu),
torch.tensor(mat),
torch.tensor(v), min_action,
max_action)
v_out = q_out.max
self.assertIsInstance(v_out, torch.Tensor)
v_out = v_out.detach().numpy()
# If mu[i] is an valid action, v_out[i] should be v[i]
mu_is_allowed = np.all((min_action < mu) * (mu < max_action), axis=1)
np.testing.assert_almost_equal(v_out[mu_is_allowed], v[mu_is_allowed])
# Otherwise, v_out[i] should be less than v[i]
mu_is_not_allowed = ~np.all(
(min_action - 1e-2 < mu) * (mu < max_action + 1e-2), axis=1)
np.testing.assert_array_less(v_out[mu_is_not_allowed],
v[mu_is_not_allowed])
def test_max_unbounded(self):
n_batch = 7
ndim_action = 3
mu = np.random.randn(n_batch, ndim_action).astype(np.float32)
mat = np.broadcast_to(
np.eye(ndim_action, dtype=np.float32)[None],
(n_batch, ndim_action, ndim_action),
)
v = np.random.randn(n_batch).astype(np.float32)
q_out = action_value.QuadraticActionValue(torch.tensor(mu),
torch.tensor(mat),
torch.tensor(v))
v_out = q_out.max
self.assertIsInstance(v_out, torch.Tensor)
v_out = v_out.detach().numpy()
np.testing.assert_almost_equal(v_out, v)
def test_getitem(self):
n_batch = 7
ndim_action = 3
mu = np.random.randn(n_batch, ndim_action).astype(np.float32)
mat = np.broadcast_to(
np.eye(ndim_action, dtype=np.float32)[None],
(n_batch, ndim_action, ndim_action),
)
v = np.random.randn(n_batch).astype(np.float32)
min_action, max_action = -1, 1
qout = action_value.QuadraticActionValue(
torch.tensor(mu),
torch.tensor(mat),
torch.tensor(v),
min_action,
max_action,
)
sliced = qout[:3]
torch_assert_allclose(sliced.mu, mu[:3])
torch_assert_allclose(sliced.mat, mat[:3])
torch_assert_allclose(sliced.v, v[:3])
torch_assert_allclose(sliced.min_action[0], min_action)
torch_assert_allclose(sliced.max_action[0], max_action)