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2_quantitative_semantics.py
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2_quantitative_semantics.py
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import numpy as np
import torch as th
from termcolor import colored
from schrl.common.path import Path
from schrl.tltl.predicate import ProgrammablePredicate
from schrl.tltl.spec import DiffTLTLSpec
def _assert(spec: DiffTLTLSpec, traj: Path, result: bool):
confidence = spec(traj)
if len(confidence.shape) > 0 and len(confidence) > 1:
# only check current state
confidence = confidence[0]
msg = f"{colored(str(traj), 'yellow')} {colored('|=', 'red')} " \
f"{spec} = {colored(str(result), 'green')}"
if (confidence > 0) == result:
print(f"[OK] {msg}")
else:
error = f"\nExpect \n" \
f"{msg} \n" \
f"but got \n" \
f"{colored(str(traj), 'yellow')} {colored('|=', 'red')} " \
f"{spec} = {colored(str(confidence.detach().cpu().numpy().item() > 0), 'green')} " \
f"(confidence: {confidence})"
assert False, error
def _optimize(spec: DiffTLTLSpec,
traj: Path,
param,
step: int = 100,
expected_result: bool = True):
initial_obj = spec(traj)
initial_param = param.detach().clone()
for i in range(step):
obj = spec(traj)
if len(obj.shape) > 0 and len(obj) > 1:
# only check current state
obj = obj[0]
obj.backward()
if expected_result:
param.data += 1e-2 * param.grad
else:
param.data -= 1e-2 * param.grad
to_num = lambda x: str(x.detach().cpu().numpy())
print(f"\t predicate value: {colored(to_num(initial_obj), 'blue')} -> "
f"{colored(to_num(spec(traj)), 'cyan')}")
print(f"\t parameter: \t \t {colored(str(to_num(initial_param)), 'blue')} -> "
f"{colored(to_num(param), 'cyan')}")
def test_forward_not():
geq_10 = ProgrammablePredicate(lambda x: x - 10, "geq_10")
spec = ~DiffTLTLSpec(geq_10)
traj1 = Path(np.array([9, 9, 9]))
traj2 = Path(np.array([11, 9.9, 9.9]))
_assert(spec, traj1, True)
_assert(spec, traj2, False)
def test_forward_and():
geq_10 = ProgrammablePredicate(lambda x: x - 10, "geq_10")
leq_11 = ProgrammablePredicate(lambda x: 11 - x, "leq_11")
spec = DiffTLTLSpec(geq_10) & DiffTLTLSpec(leq_11)
traj1 = Path(np.array([9, 9, 9]))
traj2 = Path(np.array([10.5]))
_assert(spec, traj1, False)
_assert(spec, traj2, True)
def test_forward_or():
leq_10 = ProgrammablePredicate(lambda x: 10 - x, "geq_10")
geq_11 = ProgrammablePredicate(lambda x: x - 11, "geq_11")
spec = DiffTLTLSpec(leq_10) | DiffTLTLSpec(geq_11)
traj1 = Path(np.array([9, 9, 9]))
traj2 = Path(np.array([10.5]))
traj3 = Path(np.array([11.5, 9]))
_assert(spec, traj1, True)
_assert(spec, traj2, False)
_assert(spec, traj3, True)
def test_forward_imply():
leq_10 = ProgrammablePredicate(lambda x: 10 - x, "leq_10")
geq_11 = ProgrammablePredicate(lambda x: x - 11, "geq_11")
spec = DiffTLTLSpec(leq_10).implies(DiffTLTLSpec(geq_11).next())
traj1 = Path(np.array([9, 12]))
traj2 = Path(np.array([9, 10]))
_assert(spec, traj1, True)
_assert(spec, traj2, False)
def test_forward_next():
leq_10 = ProgrammablePredicate(lambda x: 10 - x, "geq_10")
spec = DiffTLTLSpec(leq_10).next()
traj1 = Path(np.array([9, 9, 9]))
traj2 = Path(np.array([8, 11, 9]))
traj3 = Path(np.array([10.5, 9, 5]))
traj4 = Path(np.array([9.5, 11]))
_assert(spec, traj1, True)
_assert(spec, traj2, False)
_assert(spec, traj3, True)
_assert(spec, traj4, False)
def test_forward_globally():
leq_10 = ProgrammablePredicate(lambda x: 10 - x, "leq_10")
spec = (DiffTLTLSpec(leq_10)).globally()
traj1 = Path(np.array([11] * 10))
traj2 = Path(np.array([9] * 10))
_assert(spec, traj1, False)
_assert(spec, traj2, True)
def test_forward_eventually():
leq_10 = ProgrammablePredicate(lambda x: 10 - x, "leq_10")
spec = (DiffTLTLSpec(leq_10)).eventually()
traj1 = Path(np.array([11] * 3))
traj2 = Path(np.array([11] * 3 + [9]))
traj3 = Path(np.array([11] * 3 + [9, 10]))
_assert(spec, traj1, False)
_assert(spec, traj2, True)
_assert(spec, traj3, True)
def test_forward_until():
leq_10 = ProgrammablePredicate(lambda x: 10 - x, "leq_10")
geq_11 = ProgrammablePredicate(lambda x: x - 11, "geq_11")
spec = DiffTLTLSpec(leq_10).until(DiffTLTLSpec(geq_11))
traj1 = Path(np.array([9] * 3 + [12, 12, 13]))
traj2 = Path(np.array([9] * 3 + [10.5, 12, 12]))
traj3 = Path(np.array([20, 20]))
_assert(spec, traj1, True)
_assert(spec, traj2, False)
_assert(spec, traj3, True)
def test_backward_not():
param = th.tensor(7.0, requires_grad=True)
x_geq_param = ProgrammablePredicate(lambda x: x - param, "(x > param)")
spec = ~DiffTLTLSpec(x_geq_param)
traj = Path(np.array([8, 9, 2]))
_assert(spec, traj, False)
_optimize(spec, traj, param)
_assert(spec, traj, True)
def test_backward_and():
param = th.tensor([7.0, 10.0], requires_grad=True)
x_geq_param1 = ProgrammablePredicate(lambda x: x - param[0], "(x > param1)")
x_leq_param2 = ProgrammablePredicate(lambda x: param[1] - x, "(x < param2)")
spec = DiffTLTLSpec(x_geq_param1) & DiffTLTLSpec(x_leq_param2)
traj = Path(np.array([5, 9, 2]))
_assert(spec, traj, False)
_optimize(spec, traj, param)
_assert(spec, traj, True)
def test_backward_or():
param = th.tensor([10.0, 7.0], requires_grad=True)
x_geq_param1 = ProgrammablePredicate(lambda x: x - param[0], "(x > param1)")
x_leq_param2 = ProgrammablePredicate(lambda x: param[1] - x, "(x < param2)")
spec = DiffTLTLSpec(x_geq_param1) | DiffTLTLSpec(x_leq_param2)
traj = Path(np.array([8, 9, 2]))
_assert(spec, traj, False)
_optimize(spec, traj, param)
_assert(spec, traj, True)
def test_backward_imply():
param = th.tensor([6.0, 5.0], requires_grad=True)
x_geq_param1 = ProgrammablePredicate(lambda x: x - param[0], "(x > param1)")
x_leq_param2 = ProgrammablePredicate(lambda x: param[1] - x, "(x < param2)")
spec = DiffTLTLSpec(x_geq_param1).implies(DiffTLTLSpec(x_leq_param2).next())
traj = Path(np.array([8, 9, 2]))
_assert(spec, traj, False)
_optimize(spec, traj, param)
_assert(spec, traj, True)
def test_backward_next():
param = th.tensor(10.0, requires_grad=True)
x_geq_param = ProgrammablePredicate(lambda x: x - param, "(x > param)")
spec = DiffTLTLSpec(x_geq_param).next()
traj = Path(np.array([10, 9, 9]))
_assert(spec, traj, False)
_optimize(spec, traj, param)
_assert(spec, traj, True)
def test_backward_globally():
param = th.tensor(10.0, requires_grad=True)
x_geq_param = ProgrammablePredicate(lambda x: x - param, "(x > param)")
spec = DiffTLTLSpec(x_geq_param).globally()
traj = Path(np.array([9, 8, 9]))
_assert(spec, traj, False)
_optimize(spec, traj, param)
_assert(spec, traj, True)
def test_backward_eventually():
param = th.tensor(10.0, requires_grad=True)
x_geq_param = ProgrammablePredicate(lambda x: x - param, "(x > param)")
spec = DiffTLTLSpec(x_geq_param).eventually()
traj = Path(np.array([9, 8, 9]))
_assert(spec, traj, False)
_optimize(spec, traj, param)
_assert(spec, traj, True)
def test_backward_until():
param = th.tensor([10.0, 8.5], requires_grad=True)
x_geq_param1 = ProgrammablePredicate(lambda x: x - param[0], "(x > param1)")
x_leq_param2 = ProgrammablePredicate(lambda x: x - param[1], "(x > param2)")
spec = DiffTLTLSpec(x_geq_param1).until(DiffTLTLSpec(x_leq_param2))
traj = Path(np.array([8, 9, 2]))
_assert(spec, traj, False)
_optimize(spec, traj, param)
_assert(spec, traj, True)
if __name__ == '__main__':
test_forward_not()
test_forward_and()
test_forward_or()
test_forward_imply()
test_forward_next()
test_forward_globally()
test_forward_eventually()
test_forward_until()
test_backward_not()
test_backward_and()
test_backward_or()
test_backward_imply()
test_backward_next()
test_backward_globally()
test_backward_eventually()
test_backward_until()