def example_simulator_01():
path = stormpy.examples.files.prism_dtmc_die
prism_program = stormpy.parse_prism_program(path)
model = stormpy.build_model(prism_program)
simulator = stormpy.simulator.create_simulator(model, seed=42)
final_outcomes = dict()
for n in range(1000):
while not simulator.is_done():
observation = simulator.step()
if observation not in final_outcomes:
final_outcomes[observation] = 1
else:
final_outcomes[observation] += 1
simulator.restart()
print(final_outcomes)
options = stormpy.BuilderOptions([])
options.set_build_state_valuations()
model = stormpy.build_sparse_model_with_options(prism_program, options)
simulator = stormpy.simulator.create_simulator(model, seed=42)
simulator.set_observation_mode(stormpy.simulator.SimulatorObservationMode.PROGRAM_LEVEL)
final_outcomes = dict()
for n in range(1000):
while not simulator.is_done():
observation = simulator.step()
if observation not in final_outcomes:
final_outcomes[observation] = 1
else:
final_outcomes[observation] += 1
simulator.restart()
print(", ".join([f"{str(k)}: {v}" for k,v in final_outcomes.items()]))
def example_simulator_02():
path = stormpy.examples.files.prism_mdp_maze
prism_program = stormpy.parse_prism_program(path)
model = stormpy.build_model(prism_program)
simulator = stormpy.simulator.create_simulator(model, seed=42)
# 5 paths of at most 20 steps.
paths = []
for m in range(5):
path = []
state, reward, labels = simulator.restart()
path = [f"{state}"]
for n in range(20):
actions = simulator.available_actions()
select_action = random.randint(0,len(actions)-1)
#print(f"Randomly select action nr: {select_action} from actions {actions}")
path.append(f"--act={actions[select_action]}-->")
state, reward, labels = simulator.step(actions[select_action])
#print(state)
path.append(f"{state}")
if simulator.is_done():
#print("Trapped!")
break
paths.append(path)
for path in paths:
print(" ".join(path))
options = stormpy.BuilderOptions()
options.set_build_state_valuations()
options.set_build_choice_labels(True)
model = stormpy.build_sparse_model_with_options(prism_program, options)
print(model)
simulator = stormpy.simulator.create_simulator(model, seed=42)
simulator.set_observation_mode(stormpy.simulator.SimulatorObservationMode.PROGRAM_LEVEL)
simulator.set_action_mode(stormpy.simulator.SimulatorActionMode.GLOBAL_NAMES)
# 5 paths of at most 20 steps.
paths = []
for m in range(5):
path = []
state, reward, labels = simulator.restart()
path = [f"{state}"]
for n in range(20):
actions = simulator.available_actions()
select_action = random.randint(0,len(actions)-1)
#print(f"Randomly select action nr: {select_action} from actions {actions}")
path.append(f"--act={actions[select_action]}-->")
state, reward, labels = simulator.step(actions[select_action])
#print(state)
path.append(f"{state}")
if simulator.is_done():
#print("Trapped!")
break
paths.append(path)
for path in paths:
print(" ".join(path))
def example_simulator_01():
path = stormpy.examples.files.prism_mdp_maze
prism_program = stormpy.parse_prism_program(path)
model = stormpy.build_model(prism_program)
simulator = stormpy.simulator.create_simulator(model, seed=42)
# 5 paths of at most 20 steps.
paths = []
for m in range(5):
path = []
state = simulator.restart()
path = [f"{state}"]
for n in range(20):
actions = simulator.available_actions()
select_action = random.randint(0,len(actions)-1)
#print(f"Randomly select action nr: {select_action} from actions {actions}")
path.append(f"--act={actions[select_action]}-->")
state = simulator.step(actions[select_action])
#print(state)
path.append(f"{state}")
if simulator.is_done():
#print("Trapped!")
break
paths.append(path)
for path in paths:
print(" ".join(path))
def example_simulator_04():
path = stormpy.examples.files.prism_mdp_coin_2_2
prism_program = stormpy.parse_prism_program(path)
#prism_program = stormpy.preprocess_symbolic_input(prism_program, [], "delay=10,fast=0.8")[0].as_prism_program()
new_prism_program = prism_program.label_unlabelled_commands(dict())
simulator = stormpy.simulator.create_simulator(new_prism_program, seed=42)
simulator.set_action_mode(
stormpy.simulator.SimulatorActionMode.GLOBAL_NAMES)
final_outcomes = dict()
for n in range(5):
while not simulator.is_done():
actions = simulator.available_actions()
print(actions)
observation, reward, labels = simulator.step(actions[0])
print(labels)
if observation not in final_outcomes:
final_outcomes[observation] = 1
else:
final_outcomes[observation] += 1
simulator.restart()
suggestions = dict()
for m in prism_program.modules:
for c in m.commands:
if not c.is_labeled:
suggestions[c.global_index] = "tau_" + str(m.name)
new_prism_program = prism_program.label_unlabelled_commands(suggestions)
simulator = stormpy.simulator.create_simulator(new_prism_program, seed=42)
simulator.set_action_mode(
stormpy.simulator.SimulatorActionMode.GLOBAL_NAMES)
final_outcomes = dict()
for n in range(5):
while not simulator.is_done():
actions = simulator.available_actions()
print(actions)
observation, reward, labels = simulator.step(actions[0])
if observation not in final_outcomes:
final_outcomes[observation] = 1
else:
final_outcomes[observation] += 1
simulator.restart()
class TestSparseSimulator:
path = stormpy.examples.files.prism_dtmc_die
prism_program = stormpy.parse_prism_program(path)
model = stormpy.build_model(prism_program)
simulator = stormpy.simulator.create_simulator(model, seed=42)
final_outcomes = dict()
for n in range(7):
while not simulator.is_done():
observation, reward, labels = simulator.step()
assert len(labels) == 2
assert "done" in labels
if observation not in final_outcomes:
final_outcomes[observation] = 1
else:
final_outcomes[observation] += 1
simulator.restart()
def example_simulator_03():
path = stormpy.examples.files.prism_mdp_firewire
prism_program = stormpy.parse_prism_program(path)
prism_program = stormpy.preprocess_symbolic_input(
prism_program, [], "delay=10,fast=0.8")[0].as_prism_program()
simulator = stormpy.simulator.create_simulator(prism_program, seed=42)
simulator.set_action_mode(
stormpy.simulator.SimulatorActionMode.GLOBAL_NAMES)
final_outcomes = dict()
for n in range(5):
while not simulator.is_done():
actions = simulator.available_actions()
observation, reward, labels = simulator.step(actions[0])
if observation not in final_outcomes:
final_outcomes[observation] = 1
else:
final_outcomes[observation] += 1
simulator.restart()