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59 lines
1.9 KiB
59 lines
1.9 KiB
import stormpy
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import stormpy.core
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import stormpy.simulator
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import stormpy.shields
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import stormpy.examples
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import stormpy.examples.files
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import random
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"""
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Simulating a model with the usage of a pre shield
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"""
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def example_pre_shield_simulator():
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path = stormpy.examples.files.prism_mdp_lava_simple
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formula_str = "<ShieldFileName, PreSafety, gamma=0.9> Pmax=? [G !\"AgentIsInLavaAndNotDone\"]"
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program = stormpy.parse_prism_program(path)
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formulas = stormpy.parse_properties_for_prism_program(formula_str, program)
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options = stormpy.BuilderOptions([p.raw_formula for p in formulas])
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options.set_build_state_valuations(True)
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options.set_build_choice_labels(True)
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options.set_build_all_labels()
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model = stormpy.build_sparse_model_with_options(program, options)
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initial_state = model.initial_states[0]
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assert initial_state == 0
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result = stormpy.model_checking(model, formulas[0], extract_scheduler=True)
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assert result.has_scheduler
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assert result.has_shield
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shield = result.shield
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pre_scheduler = shield.construct()
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simulator = stormpy.simulator.create_simulator(model, seed=42)
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final_outcomes = dict()
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for n in range(1000):
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while not simulator.is_done():
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current_state = simulator.get_current_state()
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choices = pre_scheduler.get_choice(current_state).choice_map
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index = random.randint(0, len(choices) - 1)
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selected_action = choices[index]
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state_string = model.state_valuations.get_string(current_state)
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print(F"Simulator is in state {state_string}. Allowed Choices are {choices}. Selected Action: {selected_action}")
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observation, reward = simulator.step(selected_action[1])
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if observation not in final_outcomes:
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final_outcomes[observation] = 1
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else:
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final_outcomes[observation] += 1
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simulator.restart()
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if __name__ == '__main__':
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example_pre_shield_simulator()
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