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import stormpy import stormpy.core import stormpy.simulator
import stormpy.shields import stormpy.logic
import stormpy.examples import stormpy.examples.files
from enum import Enum from abc import ABC
import re import sys import tempfile, datetime, shutil
import gymnasium as gym
from minigrid.core.actions import Actions from minigrid.core.state import to_state
import os import time
import argparse
def tic(): #Homemade version of matlab tic and toc functions: https://stackoverflow.com/a/18903019 global startTime_for_tictoc startTime_for_tictoc = time.time()
def toc(): if 'startTime_for_tictoc' in globals(): print("Elapsed time is " + str(time.time() - startTime_for_tictoc) + " seconds.") else: print("Toc: start time not set")
class ShieldingConfig(Enum): Training = 'training' Evaluation = 'evaluation' Disabled = 'none' Full = 'full'
def __str__(self) -> str: return self.value
class ShieldHandler(ABC): def __init__(self) -> None: pass def create_shield(self, **kwargs) -> dict: pass
class MiniGridShieldHandler(ShieldHandler): def __init__(self, grid_to_prism_binary, grid_file, prism_path, formula, prism_config=None, shield_value=0.9, shield_comparison='absolute', nocleanup=False) -> None: self.tmp_dir_name = f"shielding_files_{datetime.datetime.now().strftime('%Y%m%dT%H%M%S')}_{next(tempfile._get_candidate_names())}" os.mkdir(self.tmp_dir_name) self.grid_file = self.tmp_dir_name + "/" + grid_file self.grid_to_prism_binary = grid_to_prism_binary self.prism_path = self.tmp_dir_name + "/" + prism_path self.prism_config = prism_config
self.formula = formula shield_comparison = stormpy.logic.ShieldComparison.ABSOLUTE if shield_comparison == "absolute" else stormpy.logic.ShieldComparison.RELATIVE self.shield_expression = stormpy.logic.ShieldExpression(stormpy.logic.ShieldingType.PRE_SAFETY, shield_comparison, shield_value)
self.nocleanup = nocleanup def __del__(self): if not self.nocleanup: shutil.rmtree(self.tmp_dir_name)
def __export_grid_to_text(self, env): with open(self.grid_file, "w") as f: f.write(env.printGrid(init=True))
def __create_prism(self): if self.prism_config is None: result = os.system(F"{self.grid_to_prism_binary} -i {self.grid_file} -o {self.prism_path}") else: result = os.system(F"{self.grid_to_prism_binary} -i {self.grid_file} -o {self.prism_path} -c {self.prism_config}")
assert result == 0, "Prism file could not be generated"
def __create_shield_dict(self): program = stormpy.parse_prism_program(self.prism_path)
formulas = stormpy.parse_properties_for_prism_program(self.formula, 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() print(f"LOG: Starting with explicit model creation...") tic() model = stormpy.build_sparse_model_with_options(program, options) toc()
print(f"LOG: Starting with model checking...") tic() result = stormpy.model_checking(model, formulas[0], extract_scheduler=True, shield_expression=self.shield_expression) toc()
assert result.has_shield shield = result.shield action_dictionary = dict() shield_scheduler = shield.construct() state_valuations = model.state_valuations choice_labeling = model.choice_labeling
#stormpy.shields.export_shield(model, shield, "current.shield")
for stateID in model.states: choice = shield_scheduler.get_choice(stateID) choices = choice.choice_map state_valuation = state_valuations.get_string(stateID) ints = dict(re.findall(r'([a-zA-Z][_a-zA-Z0-9]+)=([a-zA-Z0-9]+)', state_valuation)) booleans = dict(re.findall(r'(\!?)([a-zA-Z][_a-zA-Z0-9]+)[\s\t]', state_valuation)) #TODO does not parse everything correctly?
if int(ints.get("previousActionAgent", 3)) != 3: continue if int(ints.get("clock", 0)) != 0: continue state = to_state(ints, booleans) action_dictionary[state] = get_allowed_actions_mask([choice_labeling.get_labels_of_choice(model.get_choice_index(stateID, choice[1])) for choice in choices])
return action_dictionary
def create_shield(self, **kwargs): env = kwargs["env"] self.__export_grid_to_text(env) self.__create_prism()
return self.__create_shield_dict()
def expname(args): return f"{datetime.datetime.now().strftime('%Y%m%dT%H%M%S')}_{args.env}_{args.shielding}_{args.shield_comparison}_{args.shield_value}_{args.expname_suffix}"
def create_log_dir(args): log_dir = f"{args.log_dir}/{expname(args)}" os.makedirs(log_dir, exist_ok=True) return log_dir
def get_allowed_actions_mask(actions): action_mask = [0.0] * 3 + [1.0] * 4 actions_labels = [label for labels in actions for label in list(labels)] for action_label in actions_labels: if "move" in action_label: action_mask[2] = 1.0 elif "left" in action_label: action_mask[0] = 1.0 elif "right" in action_label: action_mask[1] = 1.0 return action_mask
def common_parser(): parser = argparse.ArgumentParser() parser.add_argument("--env", help="gym environment to load", default="MiniGrid-LavaSlipperyCliff-16x13-v0")
parser.add_argument("--grid_file", default="grid.txt") parser.add_argument("--prism_output_file", default="grid.prism") parser.add_argument("--log_dir", default="../log_results/") parser.add_argument("--formula", default="Pmax=? [G !AgentIsOnLava]") parser.add_argument("--shielding", type=ShieldingConfig, choices=list(ShieldingConfig), default=ShieldingConfig.Full) parser.add_argument("--steps", default=20_000, type=int) parser.add_argument("--shield_creation_at_reset", action=argparse.BooleanOptionalAction) parser.add_argument("--prism_config", default=None) parser.add_argument("--shield_value", default=0.9, type=float) parser.add_argument("--shield_comparison", default='absolute', choices=['relative', 'absolute']) parser.add_argument("--nocleanup", action=argparse.BooleanOptionalAction) parser.add_argument("--expname_suffix", default="") return parser
class MiniWrapper(gym.Wrapper): def __init__(self, env): super().__init__(env) self.env = env
def reset(self, *, seed=None, options=None): obs, info = self.env.reset(seed=seed, options=options) return obs.transpose(1,0,2), info
def observations(self, obs): return obs
def step(self, action): obs, reward, terminated, truncated, info = self.env.step(action) return obs.transpose(1,0,2), reward, terminated, truncated, info
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