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clean up single objective reward bounded case

main
TimQu 8 years ago
parent
016fedd58e
commit
117d1b5c99
4 changed files with 120 additions and 121 deletions
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  1. 12
      src/storm/modelchecker/multiobjective/pcaa/SparseMdpRewardBoundedPcaaWeightVectorChecker.cpp
  2. 10
      src/storm/modelchecker/multiobjective/rewardbounded/MultiDimensionalRewardUnfolding.cpp
  3. 20
      src/storm/modelchecker/multiobjective/rewardbounded/MultiDimensionalRewardUnfolding.h
  4. 199
      src/storm/modelchecker/prctl/helper/SparseMdpPrctlHelper.cpp

12
src/storm/modelchecker/multiobjective/pcaa/SparseMdpRewardBoundedPcaaWeightVectorChecker.cpp
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@ -49,17 +49,7 @@ namespace storm {
auto initEpoch = rewardUnfolding.getStartEpoch();
auto epochOrder = rewardUnfolding.getEpochComputationOrder(initEpoch);
EpochCheckingData cachedData;
ValueType precision = storm::utility::convertNumber<ValueType>(storm::settings::getModule<storm::settings::modules::GeneralSettings>().getPrecision());
uint64_t epochCount = 0;
for (uint64_t dim = 0; dim < rewardUnfolding.getEpochManager().getDimensionCount(); ++dim) {
epochCount += rewardUnfolding.getEpochManager().getDimensionOfEpoch(initEpoch, dim) + 1;
}
if (storm::settings::getModule<storm::settings::modules::GeneralSettings>().isSoundSet()) {
precision = precision / storm::utility::convertNumber<ValueType>(epochCount);
}
if (numChecks == 1) {
STORM_PRINT_AND_LOG("Objective/Dimension/Epoch count is: " << this->objectives.size() << "/" << rewardUnfolding.getEpochManager().getDimensionCount() << "/" << epochOrder.size() << "." << std::endl);
}
ValueType precision = rewardUnfolding.getRequiredEpochModelPrecision(initEpoch, storm::utility::convertNumber<ValueType>(storm::settings::getModule<storm::settings::modules::GeneralSettings>().getPrecision()));
storm::utility::ProgressMeasurement progress("epochs");
progress.setMaxCount(epochOrder.size());

10
src/storm/modelchecker/multiobjective/rewardbounded/MultiDimensionalRewardUnfolding.cpp
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@ -537,6 +537,16 @@ namespace storm {
return stringstream.str();
}
template<typename ValueType, bool SingleObjectiveMode>
ValueType MultiDimensionalRewardUnfolding<ValueType, SingleObjectiveMode>::getRequiredEpochModelPrecision(Epoch const& startEpoch, ValueType const& precision) {
// if (storm::settings::getModule<storm::settings::modules::GeneralSettings>().isSoundSet()) {
uint64_t sumOfDimensions = 0;
for (uint64_t dim = 0; dim < epochManager.getDimensionCount(); ++dim) {
sumOfDimensions += epochManager.getDimensionOfEpoch(startEpoch, dim) + 1;
}
return precision / storm::utility::convertNumber<ValueType>(sumOfDimensions);
}
template<typename ValueType, bool SingleObjectiveMode>
void MultiDimensionalRewardUnfolding<ValueType, SingleObjectiveMode>::setSolutionForCurrentEpoch(std::vector<SolutionType>&& inStateSolutions) {
STORM_LOG_ASSERT(currentEpoch, "Tried to set a solution for the current epoch, but no epoch was specified before.");

20
src/storm/modelchecker/multiobjective/rewardbounded/MultiDimensionalRewardUnfolding.h
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@ -46,25 +46,18 @@ namespace storm {
MultiDimensionalRewardUnfolding(storm::models::sparse::Mdp<ValueType> const& model, std::vector<storm::modelchecker::multiobjective::Objective<ValueType>> const& objectives);
MultiDimensionalRewardUnfolding(storm::models::sparse::Mdp<ValueType> const& model, std::shared_ptr<storm::logic::OperatorFormula const> objectiveFormula);
~MultiDimensionalRewardUnfolding() {
std::cout << "Implicit unfolding statistics: " << std::endl;
std::cout << " Memory Product size: " << productModel->getProduct().getNumberOfStates() << std::endl;
std::cout << " maxSolutionsStored: " << maxSolutionsStored << std::endl;
std::cout << "Occurring Epoch model sizes: ";
for (auto const& i : epochModelSizes) {
std::cout << i << " ";
}
std::cout << std::endl;
std::cout << "---------------------------------------------" << std::endl;
std::cout << std::endl;
}
~MultiDimensionalRewardUnfolding() = default;
Epoch getStartEpoch();
std::vector<Epoch> getEpochComputationOrder(Epoch const& startEpoch);
EpochModel& setCurrentEpoch(Epoch const& epoch);
/*!
* Returns the precision required for the analyzis of each epoch model in order to achieve the given overall precision
*/
ValueType getRequiredEpochModelPrecision(Epoch const& startEpoch, ValueType const& precision);
void setSolutionForCurrentEpoch(std::vector<SolutionType>&& inStateSolutions);
SolutionType const& getInitialStateResult(Epoch const& epoch); // Assumes that the initial state is unique
SolutionType const& getInitialStateResult(Epoch const& epoch, uint64_t initialStateIndex);
@ -79,7 +72,6 @@ namespace storm {
void initializeObjectives(std::vector<Epoch>& epochSteps);
void computeMaxDimensionValues();
void initializeMemoryProduct(std::vector<Epoch> const& epochSteps);

199
src/storm/modelchecker/prctl/helper/SparseMdpPrctlHelper.cpp
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@ -82,6 +82,105 @@ namespace storm {
return result;
}
template<typename ValueType>
std::vector<ValueType> analyzeTrivialEpochModel(OptimizationDirection dir, typename storm::modelchecker::multiobjective::MultiDimensionalRewardUnfolding<ValueType, true>::EpochModel& epochModel) {
// Assert that the epoch model is indeed trivial
assert(epochModel.epochMatrix.getEntryCount() == 0);
std::vector<ValueType> epochResult;
epochResult.reserve(epochModel.epochInStates.getNumberOfSetBits());
auto stepSolutionIt = epochModel.stepSolutions.begin();
auto stepChoiceIt = epochModel.stepChoices.begin();
for (auto const& state : epochModel.epochInStates) {
// Obtain the best choice for this state
ValueType bestValue;
uint64_t lastChoice = epochModel.epochMatrix.getRowGroupIndices()[state + 1];
bool isFirstChoice = true;
for (uint64_t choice = epochModel.epochMatrix.getRowGroupIndices()[state]; choice < lastChoice; ++choice) {
while (*stepChoiceIt < choice) {
++stepChoiceIt;
++stepSolutionIt;
}
ValueType choiceValue = storm::utility::zero<ValueType>();
if (epochModel.objectiveRewardFilter.front().get(choice)) {
choiceValue += epochModel.objectiveRewards.front()[choice];
}
if (*stepChoiceIt == choice) {
choiceValue += *stepSolutionIt;
}
if (isFirstChoice) {
bestValue = std::move(choiceValue);
isFirstChoice = false;
} else {
if (storm::solver::minimize(dir)) {
if (choiceValue < bestValue) {
bestValue = std::move(choiceValue);
}
} else {
if (choiceValue > bestValue) {
bestValue = std::move(choiceValue);
}
}
}
}
// Insert the solution w.r.t. this choice
epochResult.push_back(std::move(bestValue));
}
return epochResult;
}
template<typename ValueType>
std::vector<ValueType> analyzeNonTrivialEpochModel(OptimizationDirection dir, typename storm::modelchecker::multiobjective::MultiDimensionalRewardUnfolding<ValueType, true>::EpochModel& epochModel, std::vector<ValueType>& x, std::vector<ValueType>& b, std::unique_ptr<storm::solver::MinMaxLinearEquationSolver<ValueType>>& minMaxSolver, storm::solver::MinMaxLinearEquationSolverFactory<ValueType> const& minMaxLinearEquationSolverFactory, ValueType const& precision, SolutionType const& type) {
// Update some data for the case that the Matrix has changed
if (epochModel.epochMatrixChanged) {
x.assign(epochModel.epochMatrix.getRowGroupCount(), storm::utility::zero<ValueType>());
minMaxSolver = minMaxLinearEquationSolverFactory.create(epochModel.epochMatrix);
minMaxSolver->setHasUniqueSolution();
minMaxSolver->setPrecision(precision);
minMaxSolver->setOptimizationDirection(dir);
minMaxSolver->setCachingEnabled(true);
minMaxSolver->setTrackScheduler(true);
auto req = minMaxSolver->getRequirements(dir);
minMaxSolver->setLowerBound(storm::utility::zero<ValueType>());
req.clearLowerBounds();
if (type == SolutionType::UntilProbabilities) {
minMaxSolver->setUpperBound(storm::utility::one<ValueType>());
req.clearUpperBounds();
} else if (type == SolutionType::ExpectedRewards) {
// TODO
STORM_LOG_WARN_COND(!req.requiresUpperBounds(), "Upper bounds for expected reward are not specified.");
}
STORM_LOG_THROW(req.empty(), storm::exceptions::UncheckedRequirementException, "At least one requirement was not checked.");
minMaxSolver->setRequirementsChecked();
} else {
auto choicesTmp = minMaxSolver->getSchedulerChoices();
minMaxSolver->setInitialScheduler(std::move(choicesTmp));
}
// Prepare the right hand side of the equation system
b.assign(epochModel.epochMatrix.getRowCount(), storm::utility::zero<ValueType>());
std::vector<ValueType> const& objectiveValues = epochModel.objectiveRewards.front();
for (auto const& choice : epochModel.objectiveRewardFilter.front()) {
b[choice] = objectiveValues[choice];
}
auto stepSolutionIt = epochModel.stepSolutions.begin();
for (auto const& choice : epochModel.stepChoices) {
b[choice] += *stepSolutionIt;
++stepSolutionIt;
}
assert(stepSolutionIt == epochModel.stepSolutions.end());
// Solve the minMax equation system
minMaxSolver->solveEquations(x, b);
return storm::utility::vector::filterVector(x, epochModel.epochInStates);
}
template<typename ValueType>
std::map<storm::storage::sparse::state_type, ValueType> SparseMdpPrctlHelper<ValueType>::computeRewardBoundedValues(SolutionType const& type, OptimizationDirection dir, storm::modelchecker::multiobjective::MultiDimensionalRewardUnfolding<ValueType, true>& rewardUnfolding, storm::storage::BitVector const& initialStates, storm::solver::MinMaxLinearEquationSolverFactory<ValueType> const& minMaxLinearEquationSolverFactory) {
storm::utility::Stopwatch swAll(true), swBuild, swCheck;
@ -92,14 +191,8 @@ namespace storm {
std::vector<ValueType> x, b;
std::unique_ptr<storm::solver::MinMaxLinearEquationSolver<ValueType>> minMaxSolver;
ValueType precision = storm::utility::convertNumber<ValueType>(storm::settings::getModule<storm::settings::modules::GeneralSettings>().getPrecision());
uint64_t epochCount = 0;
for (uint64_t dim = 0; dim < rewardUnfolding.getEpochManager().getDimensionCount(); ++dim) {
epochCount += rewardUnfolding.getEpochManager().getDimensionOfEpoch(initEpoch, dim) + 1;
}
if (storm::settings::getModule<storm::settings::modules::GeneralSettings>().isSoundSet()) {
precision = precision / storm::utility::convertNumber<ValueType>(epochCount);
}
ValueType precision = rewardUnfolding.getRequiredEpochModelPrecision(initEpoch, storm::utility::convertNumber<ValueType>(storm::settings::getModule<storm::settings::modules::GeneralSettings>().getPrecision()));
storm::utility::ProgressMeasurement progress("epochs");
progress.setMaxCount(epochOrder.size());
progress.startNewMeasurement(0);
@ -110,95 +203,9 @@ namespace storm {
swBuild.stop(); swCheck.start();
// If the epoch matrix is empty we do not need to solve a linear equation system
if (epochModel.epochMatrix.getEntryCount() == 0) {
std::vector<ValueType> epochResult;
epochResult.reserve(epochModel.epochInStates.getNumberOfSetBits());
auto stepSolutionIt = epochModel.stepSolutions.begin();
auto stepChoiceIt = epochModel.stepChoices.begin();
for (auto const& state : epochModel.epochInStates) {
// Obtain the best choice for this state
ValueType bestValue;
uint64_t lastChoice = epochModel.epochMatrix.getRowGroupIndices()[state + 1];
bool isFirstChoice = true;
for (uint64_t choice = epochModel.epochMatrix.getRowGroupIndices()[state]; choice < lastChoice; ++choice) {
while (*stepChoiceIt < choice) {
++stepChoiceIt;
++stepSolutionIt;
}
ValueType choiceValue = storm::utility::zero<ValueType>();
if (epochModel.objectiveRewardFilter.front().get(choice)) {
choiceValue += epochModel.objectiveRewards.front()[choice];
}
if (*stepChoiceIt == choice) {
choiceValue += *stepSolutionIt;
}
if (isFirstChoice) {
bestValue = std::move(choiceValue);
isFirstChoice = false;
} else {
if (storm::solver::minimize(dir)) {
if (choiceValue < bestValue) {
bestValue = std::move(choiceValue);
}
} else {
if (choiceValue > bestValue) {
bestValue = std::move(choiceValue);
}
}
}
}
// Insert the solution w.r.t. this choice
epochResult.push_back(std::move(bestValue));
}
rewardUnfolding.setSolutionForCurrentEpoch(std::move(epochResult));
rewardUnfolding.setSolutionForCurrentEpoch(analyzeTrivialEpochModel<ValueType>(dir, epochModel));
} else {
// Update some data for the case that the Matrix has changed
if (epochModel.epochMatrixChanged) {
x.assign(epochModel.epochMatrix.getRowGroupCount(), storm::utility::zero<ValueType>());
minMaxSolver = minMaxLinearEquationSolverFactory.create(epochModel.epochMatrix);
minMaxSolver->setHasUniqueSolution();
minMaxSolver->setPrecision(precision);
minMaxSolver->setOptimizationDirection(dir);
minMaxSolver->setCachingEnabled(true);
minMaxSolver->setTrackScheduler(true);
auto req = minMaxSolver->getRequirements(dir);
minMaxSolver->setLowerBound(storm::utility::zero<ValueType>());
req.clearLowerBounds();
if (type == SolutionType::UntilProbabilities) {
minMaxSolver->setUpperBound(storm::utility::one<ValueType>());
req.clearUpperBounds();
} else if (type == SolutionType::ExpectedRewards) {
// TODO
STORM_LOG_WARN_COND(!req.requiresUpperBounds(), "Upper bounds for expected reward are not specified.");
}
STORM_LOG_THROW(req.empty(), storm::exceptions::UncheckedRequirementException, "At least one requirement was not checked.");
minMaxSolver->setRequirementsChecked();
} else {
auto choicesTmp = minMaxSolver->getSchedulerChoices();
minMaxSolver->setInitialScheduler(std::move(choicesTmp));
}
// Prepare the right hand side of the equation system
b.assign(epochModel.epochMatrix.getRowCount(), storm::utility::zero<ValueType>());
std::vector<ValueType> const& objectiveValues = epochModel.objectiveRewards.front();
for (auto const& choice : epochModel.objectiveRewardFilter.front()) {
b[choice] = objectiveValues[choice];
}
auto stepSolutionIt = epochModel.stepSolutions.begin();
for (auto const& choice : epochModel.stepChoices) {
b[choice] += *stepSolutionIt;
++stepSolutionIt;
}
assert(stepSolutionIt == epochModel.stepSolutions.end());
// Solve the minMax equation system
minMaxSolver->solveEquations(x, b);
// Plug in the result into the reward unfolding
rewardUnfolding.setSolutionForCurrentEpoch(storm::utility::vector::filterVector(x, epochModel.epochInStates));
rewardUnfolding.setSolutionForCurrentEpoch(analyzeNonTrivialEpochModel<ValueType>(dir, epochModel, x, b, minMaxSolver, minMaxLinearEquationSolverFactory, precision, type));
}
swCheck.stop();
++numCheckedEpochs;

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