You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
64 lines
2.2 KiB
64 lines
2.2 KiB
import stormpy
|
|
import stormpy.core
|
|
import stormpy.simulator
|
|
|
|
import stormpy.shields
|
|
|
|
import stormpy.examples
|
|
import stormpy.examples.files
|
|
|
|
import random
|
|
|
|
"""
|
|
Simulating a model with the usage of a pre shield
|
|
"""
|
|
|
|
def example_pre_shield_simulator():
|
|
path = stormpy.examples.files.prism_mdp_cliff_walking
|
|
formula_str = "Pmax=? [G !\"AgentIsInLavaAndNotDone\"]"
|
|
|
|
program = stormpy.parse_prism_program(path)
|
|
formulas = stormpy.parse_properties_for_prism_program(formula_str, program)
|
|
|
|
options = stormpy.BuilderOptions([p.raw_formula for p in formulas])
|
|
options.set_build_state_valuations(True)
|
|
options.set_build_choice_labels(True)
|
|
options.set_build_all_labels()
|
|
model = stormpy.build_sparse_model_with_options(program, options)
|
|
|
|
initial_state = model.initial_states[0]
|
|
assert initial_state == 0
|
|
|
|
shield_specification = stormpy.logic.ShieldExpression(stormpy.logic.ShieldingType.PRE_SAFETY, stormpy.logic.ShieldComparison.RELATIVE, 0.9)
|
|
result = stormpy.model_checking(model, formulas[0], extract_scheduler=True, shield_expression=shield_specification)
|
|
|
|
assert result.has_scheduler
|
|
assert result.has_shield
|
|
|
|
shield = result.shield
|
|
|
|
pre_scheduler = shield.construct()
|
|
|
|
simulator = stormpy.simulator.create_simulator(model, seed=42)
|
|
|
|
while not simulator.is_done():
|
|
current_state = simulator.get_current_state()
|
|
state_string = model.state_valuations.get_string(current_state)
|
|
print(F"Simulator is in state {state_string}.")
|
|
choices = [x for x in pre_scheduler.get_choice(current_state).choice_map if x[0] > 0]
|
|
choice_labels = [model.choice_labeling.get_labels_of_choice(model.get_choice_index(current_state, choice[1])) for choice in choices]
|
|
|
|
if not choices:
|
|
break
|
|
|
|
index = random.randint(0, len(choices) - 1)
|
|
selected_action = choices[index]
|
|
choice_label = model.choice_labeling.get_labels_of_choice(model.get_choice_index(current_state, selected_action[1]))
|
|
print(F"Allowed Choices are {choice_labels}. Selected Action: {choice_label}")
|
|
observation, reward = simulator.step(selected_action[1])
|
|
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
example_pre_shield_simulator()
|