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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_lava_simple
formula_str = "<ShieldFileName, PreSafety, gamma=0.9> 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
result = stormpy.model_checking(model, formulas[0], extract_scheduler=True)
assert result.has_scheduler
assert result.has_shield
shield = result.shield
pre_scheduler = shield.construct()
simulator = stormpy.simulator.create_simulator(model, seed=42)
final_outcomes = dict()
for n in range(1000):
while not simulator.is_done():
current_state = simulator.get_current_state()
choices = pre_scheduler.get_choice(current_state).choice_map
index = random.randint(0, len(choices) - 1)
selected_action = choices[index]
state_string = model.state_valuations.get_string(current_state)
print(F"Simulator is in state {state_string}. Allowed Choices are {choices}. Selected Action: {selected_action}")
observation, reward = simulator.step(selected_action[1])
if observation not in final_outcomes:
final_outcomes[observation] = 1
else:
final_outcomes[observation] += 1
simulator.restart()
if __name__ == '__main__':
example_pre_shield_simulator()