The source code and dockerfile for the GSW2024 AI Lab.
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import stormpy
import stormpy.logic
from helpers.helper import get_example_path
import math
class TestScheduler:
def test_scheduler_mdp(self):
program = stormpy.parse_prism_program(get_example_path("mdp", "coin2-2.nm"))
formulas = stormpy.parse_properties_for_prism_program("Pmin=? [ F \"finished\" & \"all_coins_equal_1\"]", program)
model = stormpy.build_model(program, formulas)
assert model.nr_states == 272
assert model.nr_transitions == 492
assert len(model.initial_states) == 1
initial_state = model.initial_states[0]
assert initial_state == 0
result = stormpy.model_checking(model, formulas[0], extract_scheduler=True)
assert result.has_scheduler
scheduler = result.scheduler
assert scheduler.memoryless
assert scheduler.memory_size == 1
assert scheduler.deterministic
for state in model.states:
choice = scheduler.get_choice(state)
assert choice.defined
assert choice.deterministic
action = choice.get_deterministic_choice()
assert 0 <= action
assert action < len(state.actions)
def test_scheduler_ma_via_mdp(self):
program = stormpy.parse_prism_program(get_example_path("ma", "simple.ma"), False, True)
formulas = stormpy.parse_properties_for_prism_program("Tmin=? [ F s=4 ]", program)
ma = stormpy.build_model(program, formulas)
assert ma.nr_states == 5
assert ma.nr_transitions == 8
assert ma.model_type == stormpy.ModelType.MA
# Convert MA to MDP
mdp, mdp_formulas = stormpy.transform_to_discrete_time_model(ma, formulas)
assert mdp.nr_states == 5
assert mdp.nr_transitions == 8
assert mdp.model_type == stormpy.ModelType.MDP
assert len(mdp.initial_states) == 1
initial_state = mdp.initial_states[0]
assert initial_state == 0
result = stormpy.model_checking(mdp, mdp_formulas[0], extract_scheduler=True)
assert math.isclose(result.at(initial_state), 0.08333333333)
assert result.has_scheduler
scheduler = result.scheduler
assert scheduler.memoryless
assert scheduler.memory_size == 1
assert scheduler.deterministic
for state in mdp.states:
choice = scheduler.get_choice(state)
assert choice.defined
assert choice.deterministic
action = choice.get_deterministic_choice()
if state.id == 0:
assert action == 1
else:
assert action == 0
def test_apply_scheduler_mdp(self):
program = stormpy.parse_prism_program(get_example_path("mdp", "coin2-2.nm"))
formulas = stormpy.parse_properties_for_prism_program("Pmin=? [ F \"finished\" & \"all_coins_equal_1\"]", program)
model = stormpy.build_model(program, formulas)
assert model.nr_states == 272
assert model.nr_transitions == 492
assert len(model.initial_states) == 1
initial_state = model.initial_states[0]
assert initial_state == 0
result = stormpy.model_checking(model, formulas[0], extract_scheduler=True)
assert result.has_scheduler
scheduler = result.scheduler
assert scheduler.memoryless
assert scheduler.memory_size == 1
assert scheduler.deterministic
assert not scheduler.partial
intermediate = model.apply_scheduler(scheduler, True)
assert intermediate.model_type == stormpy.ModelType.MDP
assert intermediate.nr_states == 126
assert intermediate.nr_transitions == 156
for state in intermediate.states:
assert len(state.actions) == 1
def test_apply_scheduler_ma(self):
program = stormpy.parse_prism_program(get_example_path("ma", "simple.ma"), False, True)
formulas = stormpy.parse_properties_for_prism_program("Tmin=? [ F s=4 ]", program)
ma = stormpy.build_model(program, formulas)
assert ma.nr_states == 5
assert ma.nr_transitions == 8
assert ma.model_type == stormpy.ModelType.MA
initial_state = ma.initial_states[0]
assert initial_state == 0
result = stormpy.model_checking(ma, formulas[0], extract_scheduler=True)
assert math.isclose(result.at(initial_state), 0.08333333333)
assert result.has_scheduler
scheduler = result.scheduler
assert scheduler.memoryless
assert scheduler.memory_size == 1
assert scheduler.deterministic
intermediate = ma.apply_scheduler(scheduler)
assert intermediate.model_type == stormpy.ModelType.MA
assert intermediate.nr_states == 3
assert intermediate.nr_transitions == 4
for state in intermediate.states:
assert len(state.actions) == 1