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Added flag to toggle caching of subsimplex and lambda values

tempestpy_adaptions
Alexander Bork 5 years ago
parent
aca676a0a5
commit
f416cc8291
2 changed files with 54 additions and 43 deletions
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  1. 80
      src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.cpp
  2. 1
      src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.h

80
src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.cpp
View File

@ -24,6 +24,7 @@ namespace storm {
cc = storm::utility::ConstantsComparator<ValueType>(storm::utility::convertNumber<ValueType>(precision), false);
useMdp = true;
maxIterations = 1000;
cacheSubsimplices = false;
}
template<typename ValueType, typename RewardModelType>
@ -81,6 +82,7 @@ namespace storm {
initPropString += "=? [F \"goal\"]";
std::vector<storm::jani::Property> propVector = storm::api::parseProperties(initPropString);
std::shared_ptr<storm::logic::Formula const> underlyingProperty = storm::api::extractFormulasFromProperties(propVector).front();
STORM_PRINT("Underlying MDP" << std::endl)
underlyingMdp.printModelInformationToStream(std::cout);
std::unique_ptr<storm::modelchecker::CheckResult> underlyingRes(
@ -90,6 +92,7 @@ namespace storm {
auto mdpResultMap = underlyingRes->asExplicitQuantitativeCheckResult<ValueType>().getValueMap();
auto underApproxModel = underlyingMdp.applyScheduler(pomdpScheduler, false);
STORM_PRINT("Random Positional Scheduler" << std::endl)
underApproxModel->printModelInformationToStream(std::cout);
std::unique_ptr<storm::modelchecker::CheckResult> underapproxRes(
storm::api::verifyWithSparseEngine<ValueType>(underApproxModel, storm::api::createTask<ValueType>(underlyingProperty, false)));
@ -138,13 +141,15 @@ namespace storm {
std::pair<std::vector<std::vector<ValueType>>, std::vector<ValueType>> initTemp = computeSubSimplexAndLambdas(initialBelief.probabilities, gridResolution);
std::vector<std::vector<ValueType>> initSubSimplex = initTemp.first;
std::vector<ValueType> initLambdas = initTemp.second;
if(cacheSubsimplices){
subSimplexCache[0] = initSubSimplex;
lambdaCache[0] = initLambdas;
bool initInserted = false;
}
std::vector<std::map<uint64_t, ValueType>> initTransitionsInBelief;
std::map<uint64_t, ValueType> initTransitionInActionBelief;
bool initInserted = false;
for (size_t j = 0; j < initLambdas.size(); ++j) {
if (!cc.isEqual(initLambdas[j], storm::utility::zero<ValueType>())) {
uint64_t searchResult = getBeliefIdInVector(beliefList, initialBelief.observation, initSubSimplex[j]);
@ -183,13 +188,14 @@ namespace storm {
mdpTransitions.push_back(initTransitionsInBelief);
}
//beliefsToBeExpanded.push_back(initialBelief.id); TODO I'm curious what happens if we do this instead of first triangulating. Should do nothing special if belief is on grid, otherwise it gets interesting
//beliefsToBeExpanded.push_back(initialBelief.id); I'm curious what happens if we do this instead of first triangulating. Should do nothing special if belief is on grid, otherwise it gets interesting
std::map<uint64_t, ValueType> weightedSumOverMap;
std::map<uint64_t, ValueType> weightedSumUnderMap;
// Expand the beliefs to generate the grid on-the-fly to avoid unreachable grid points
while (!beliefsToBeExpanded.empty()) {
// TODO add direct generation of transition matrix
uint64_t currId = beliefsToBeExpanded.front();
beliefsToBeExpanded.pop_front();
bool isTarget = beliefIsTarget[currId];
@ -230,8 +236,7 @@ namespace storm {
//Triangulate here and put the possibly resulting belief in the grid
std::vector<std::vector<ValueType>> subSimplex;
std::vector<ValueType> lambdas;
if (subSimplexCache.count(idNextBelief) > 0) {
// TODO is this necessary here? Think later
if (cacheSubsimplices && subSimplexCache.count(idNextBelief) > 0) {
subSimplex = subSimplexCache[idNextBelief];
lambdas = lambdaCache[idNextBelief];
} else {
@ -239,9 +244,11 @@ namespace storm {
beliefList[idNextBelief].probabilities, gridResolution);
subSimplex = temp.first;
lambdas = temp.second;
if(cacheSubsimplices){
subSimplexCache[idNextBelief] = subSimplex;
lambdaCache[idNextBelief] = lambdas;
}
}
for (size_t j = 0; j < lambdas.size(); ++j) {
if (!cc.isEqual(lambdas[j], storm::utility::zero<ValueType>())) {
@ -307,6 +314,8 @@ namespace storm {
STORM_PRINT("#Believes in List: " << beliefList.size() << std::endl)
STORM_PRINT("Belief space expansion took " << expansionTimer << std::endl)
//auto overApprox = overApproximationValueIteration(pomdp, beliefList, beliefGrid, beliefIsTarget, observationProbabilities, nextBelieves, beliefActionRewards, subSimplexCache, lambdaCache,result, chosenActions, gridResolution, min, computeRewards);
storm::models::sparse::StateLabeling mdpLabeling(mdpTransitions.size());
mdpLabeling.addLabel("init");
mdpLabeling.addLabel("target");
@ -334,29 +343,6 @@ namespace storm {
}
overApproxMdp.printModelInformationToStream(std::cout);
/*
storm::utility::Stopwatch overApproxTimer(true);
auto overApprox = overApproximationValueIteration(pomdp, beliefList, beliefGrid, beliefIsTarget, observationProbabilities, nextBelieves, beliefActionRewards,
subSimplexCache, lambdaCache,
result, chosenActions, gridResolution, min, computeRewards);
overApproxTimer.stop();*/
auto underApprox = storm::utility::zero<ValueType>();
auto overApprox = storm::utility::one<ValueType>();
/*
storm::utility::Stopwatch underApproxTimer(true);
ValueType underApprox = computeUnderapproximationWithMDP(pomdp, beliefList, beliefIsTarget, targetObservations, observationProbabilities, nextBelieves,
result, chosenActions, gridResolution, initialBelief.id, min, computeRewards);
underApproxTimer.stop();
STORM_PRINT("Time Overapproximation: " << overApproxTimer
<< std::endl
<< "Time Underapproximation: " << underApproxTimer
<< std::endl);
STORM_PRINT("Over-Approximation Result: " << overApprox << std::endl);
STORM_PRINT("Under-Approximation Result: " << underApprox << std::endl);*/
auto model = std::make_shared<storm::models::sparse::Mdp<ValueType, RewardModelType>>(overApproxMdp);
auto modelPtr = std::static_pointer_cast<storm::models::sparse::Model<ValueType, RewardModelType>>(model);
std::vector<std::string> parameterNames;
@ -367,17 +353,26 @@ namespace storm {
propertyString += "=? [F \"target\"]";
std::vector<storm::jani::Property> propertyVector = storm::api::parseProperties(propertyString);
std::shared_ptr<storm::logic::Formula const> property = storm::api::extractFormulasFromProperties(propertyVector).front();
auto task = storm::api::createTask<ValueType>(property, true);
auto hint = storm::modelchecker::ExplicitModelCheckerHint<ValueType>();
hint.setResultHint(hintVector);
auto hintPtr = std::make_shared<storm::modelchecker::ExplicitModelCheckerHint<ValueType>>(hint);
task.setHint(hintPtr);
storm::utility::Stopwatch overApproxTimer(true);
std::unique_ptr<storm::modelchecker::CheckResult> res(storm::api::verifyWithSparseEngine<ValueType>(model, task));
overApproxTimer.stop();
STORM_LOG_ASSERT(res, "Result not exist.");
res->filter(storm::modelchecker::ExplicitQualitativeCheckResult(model->getInitialStates()));
STORM_PRINT("OverApprox MDP: " << (res->asExplicitQuantitativeCheckResult<ValueType>().getValueMap().begin()->second) << std::endl);
auto overApprox = res->asExplicitQuantitativeCheckResult<ValueType>().getValueMap().begin()->second;
/* storm::utility::Stopwatch underApproxTimer(true);
ValueType underApprox = computeUnderapproximationWithMDP(pomdp, beliefList, beliefIsTarget, targetObservations, observationProbabilities, nextBelieves,
result, chosenActions, gridResolution, initialBelief.id, min, computeRewards);
underApproxTimer.stop();*/
STORM_PRINT("Time Overapproximation: " << overApproxTimer << std::endl)
auto underApprox = storm::utility::zero<ValueType>();
STORM_PRINT("Over-Approximation Result: " << overApprox << std::endl);
STORM_PRINT("Under-Approximation Result: " << underApprox << std::endl);
return std::make_unique<POMDPCheckResult<ValueType>>(POMDPCheckResult<ValueType>{overApprox, underApprox});
}
@ -425,7 +420,7 @@ namespace storm {
// cache the values to not always re-calculate
std::vector<std::vector<ValueType>> subSimplex;
std::vector<ValueType> lambdas;
if (subSimplexCache.count(nextBelief.id) > 0) {
if (cacheSubsimplices && subSimplexCache.count(nextBelief.id) > 0) {
subSimplex = subSimplexCache[nextBelief.id];
lambdas = lambdaCache[nextBelief.id];
} else {
@ -433,9 +428,11 @@ namespace storm {
gridResolution);
subSimplex = temp.first;
lambdas = temp.second;
if(cacheSubsimplices) {
subSimplexCache[nextBelief.id] = subSimplex;
lambdaCache[nextBelief.id] = lambdas;
}
}
auto sum = storm::utility::zero<ValueType>();
for (size_t j = 0; j < lambdas.size(); ++j) {
if (!cc.isEqual(lambdas[j], storm::utility::zero<ValueType>())) {
@ -477,11 +474,21 @@ namespace storm {
STORM_PRINT("Overapproximation took " << iteration << " iterations" << std::endl);
std::vector<ValueType> initialLambda;
std::vector<std::vector<ValueType>> initialSubsimplex;
if(cacheSubsimplices){
initialLambda = lambdaCache[0];
initialSubsimplex = subSimplexCache[0];
} else {
auto temp = computeSubSimplexAndLambdas(beliefList[0].probabilities, gridResolution);
initialSubsimplex= temp.first;
initialLambda = temp.second;
}
auto overApprox = storm::utility::zero<ValueType>();
for (size_t j = 0; j < lambdaCache[0].size(); ++j) {
if (lambdaCache[0][j] != storm::utility::zero<ValueType>()) {
overApprox += lambdaCache[0][j] * result_backup[getBeliefIdInVector(beliefGrid, beliefList[0].observation, subSimplexCache[0][j])];
for (size_t j = 0; j < initialLambda.size(); ++j) {
if (initialLambda[j] != storm::utility::zero<ValueType>()) {
overApprox += initialLambda[j] * result_backup[getBeliefIdInVector(beliefGrid, beliefList[0].observation, initialSubsimplex[j])];
}
}
return overApprox;
@ -541,8 +548,10 @@ namespace storm {
std::map<uint64_t, std::vector<ValueType>> lambdaCache;
std::pair<std::vector<std::vector<ValueType>>, std::vector<ValueType>> temp = computeSubSimplexAndLambdas(initialBelief.probabilities, gridResolution);
if(cacheSubsimplices) {
subSimplexCache[0] = temp.first;
lambdaCache[0] = temp.second;
}
storm::utility::Stopwatch nextBeliefGeneration(true);
for (size_t i = 0; i < beliefGrid.size(); ++i) {
@ -553,6 +562,7 @@ namespace storm {
} else {
result.emplace(std::make_pair(currentBelief.id, storm::utility::zero<ValueType>()));
//TODO put this in extra function
// As we need to grab some parameters which are the same for all states with the same observation, we simply select some state as the representative
uint64_t representativeState = pomdp.getStatesWithObservation(currentBelief.observation).front();
uint64_t numChoices = pomdp.getNumberOfChoices(representativeState);

1
src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.h
View File

@ -253,6 +253,7 @@ namespace storm {
storm::utility::ConstantsComparator<ValueType> cc;
double precision;
bool useMdp;
bool cacheSubsimplices;
uint64_t maxIterations;
};

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