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Add barrier loss to total_loss() #443

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Add barrier loss to total_loss() #443

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46 changes: 44 additions & 2 deletions neural_network_lyapunov/test/test_train_lyapunov_barrier.py
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Original file line number Diff line number Diff line change
Expand Up @@ -232,13 +232,23 @@ def test_total_loss(self):
positivity_state_samples = state_samples_all.clone()
derivative_state_samples = state_samples_all.clone()
derivative_state_samples_next = state_samples_next.clone()
safe_state_samples = torch.empty((0, dut.x_dim()), dtype=dut.dtype())
unsafe_state_samples = torch.empty((0, dut.x_dim()), dtype=dut.dtype())
barrier_derivative_state_samples = torch.empty((0, dut.x_dim()),
dtype=dut.dtype())

total_loss_return = dut.total_loss(
positivity_state_samples, derivative_state_samples,
state_samples_next, dut.lyapunov_positivity_sample_cost_weight,
dut.lyapunov_derivative_sample_cost_weight,
dut.lyapunov_positivity_mip_cost_weight,
dut.lyapunov_derivative_mip_cost_weight,
dut.boundary_value_gap_mip_cost_weight)
dut.boundary_value_gap_mip_cost_weight, safe_state_samples,
unsafe_state_samples, barrier_derivative_state_samples,
dut.safe_sample_cost_weight, dut.unsafe_sample_cost_weight,
dut.barrier_derivative_sample_cost_weight,
dut.safe_mip_cost_weight, dut.unsafe_mip_cost_weight,
dut.barrier_derivative_mip_cost_weight)

self.assertEqual(
positivity_state_samples.shape[0] + 1,
Expand Down Expand Up @@ -520,7 +530,7 @@ def test_solve_barrier_derivative_mip(self):
adversarial, dut.barrier_x_star, dut.barrier_c,
dut.barrier_epsilon).detach().numpy())

def test_barrier_loss(self):
def test_barrier_loss1(self):
dut = train_lyapunov_barrier.Trainer()
dut.add_barrier(self.barrier_system,
x_star=(self.system.x_lo * 0.25 +
Expand Down Expand Up @@ -569,6 +579,38 @@ def test_barrier_loss(self):
self.assertGreater(barrier_loss.derivative_state_samples.shape[0],
num_derivative_state_samples)

def test_barrier_loss2(self):
dut = train_lyapunov_barrier.Trainer()
dut.add_barrier(self.barrier_system,
x_star=(self.system.x_lo * 0.25 +
self.system.x_up * 0.75),
c=0.1,
barrier_epsilon=0.3)

safe_state_samples = torch.empty((0, self.barrier_system.system.x_dim),
dtype=self.dtype)
unsafe_state_samples = torch.empty(
(0, self.barrier_system.system.x_dim), dtype=self.dtype)
derivative_state_samples = torch.empty(
(0, self.barrier_system.system.x_dim), dtype=self.dtype)
safe_sample_cost_weight = 1.
unsafe_sample_cost_weight = 2.
derivative_sample_cost_weight = 3.
safe_mip_cost_weight = None
unsafe_mip_cost_weight = None
derivative_mip_cost_weight = None
barrier_loss = dut.compute_barrier_loss(
safe_state_samples, unsafe_state_samples, derivative_state_samples,
safe_sample_cost_weight, unsafe_sample_cost_weight,
derivative_sample_cost_weight, safe_mip_cost_weight,
unsafe_mip_cost_weight, derivative_mip_cost_weight)
self.assertEqual(barrier_loss.safe_sample_loss.item(), 0)
self.assertEqual(barrier_loss.unsafe_sample_loss.item(), 0)
self.assertEqual(barrier_loss.derivative_sample_loss.item(), 0)
self.assertIsNone(barrier_loss.safe_mip_loss)
self.assertIsNone(barrier_loss.unsafe_mip_loss)
self.assertIsNone(barrier_loss.derivative_mip_loss)


class TestTrainValueApproximator(unittest.TestCase):
def setUp(self):
Expand Down
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