sp
/
tempest
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
Browse Source

Using the new reward functionalities of BliefGrid. This also fixes setting rewards in a wrong way (previously, the same reward was assigned to states with the same observation).

main
Tim Quatmann 6 years ago
parent
98bb48d3c5
commit
a3e92d2f72
1 changed files with 39 additions and 32 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 71
      src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.cpp

71
src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.cpp
View File

@ -137,7 +137,28 @@ namespace storm {
stream << "##########################################" << std::endl;
}
std::shared_ptr<storm::logic::Formula const> createStandardProperty(bool min, bool computeRewards) {
std::string propertyString = computeRewards ? "R" : "P";
propertyString += min ? "min" : "max";
propertyString += "=? [F \"target\"]";
std::vector<storm::jani::Property> propertyVector = storm::api::parseProperties(propertyString);
return storm::api::extractFormulasFromProperties(propertyVector).front();
}
template<typename ValueType>
storm::modelchecker::CheckTask<storm::logic::Formula, ValueType> createStandardCheckTask(std::shared_ptr<storm::logic::Formula const>& property, std::vector<ValueType>&& hintVector) {
//Note: The property should not run out of scope after calling this because the task only stores the property by reference.
// Therefore, this method needs the property by reference (and not const reference)
auto task = storm::api::createTask<ValueType>(property, false);
if (!hintVector.empty()) {
auto hint = storm::modelchecker::ExplicitModelCheckerHint<ValueType>();
hint.setResultHint(std::move(hintVector));
auto hintPtr = std::make_shared<storm::modelchecker::ExplicitModelCheckerHint<ValueType>>(hint);
task.setHint(hintPtr);
}
return task;
}
template<typename ValueType, typename RewardModelType>
std::unique_ptr<POMDPCheckResult<ValueType>>
@ -360,6 +381,10 @@ namespace storm {
}
storm::storage::BeliefGrid<storm::models::sparse::Pomdp<ValueType>> beliefGrid(pomdp, options.numericPrecision);
if (computeRewards) {
beliefGrid.setRewardModel();
}
bsmap_type beliefStateMap;
std::deque<uint64_t> beliefsToBeExpanded;
@ -520,37 +545,27 @@ namespace storm {
storm::storage::sparse::ModelComponents<ValueType, RewardModelType> modelComponents(mdpTransitionsBuilder.build(mdpMatrixRow, nextMdpStateId, nextMdpStateId), std::move(mdpLabeling));
auto overApproxMdp = std::make_shared<storm::models::sparse::Mdp<ValueType, RewardModelType>>(std::move(modelComponents));
if (computeRewards) {
storm::models::sparse::StandardRewardModel<ValueType> mdpRewardModel(boost::none, std::vector<ValueType>(mdpMatrixRow));
storm::models::sparse::StandardRewardModel<ValueType> mdpRewardModel(boost::none, std::vector<ValueType>(mdpMatrixRow, storm::utility::zero<ValueType>()));
for (auto const &iter : beliefStateMap.left) {
if (fullyExpandedStates.get(iter.second)) {
auto currentBelief = beliefGrid.getGridPoint(iter.first);
auto const& currentBelief = beliefGrid.getGridPoint(iter.first);
auto representativeState = currentBelief.begin()->first;
for (uint64_t action = 0; action < pomdp.getNumberOfChoices(representativeState); ++action) {
// Add the reward
uint64_t mdpChoice = overApproxMdp->getChoiceIndex(storm::storage::StateActionPair(iter.second, action));
uint64_t pomdpChoice = pomdp.getChoiceIndex(storm::storage::StateActionPair(representativeState, action));
mdpRewardModel.setStateActionReward(mdpChoice, getRewardAfterAction(pomdpChoice, currentBelief));
mdpRewardModel.setStateActionReward(mdpChoice, beliefGrid.getBeliefActionReward(currentBelief, action));
}
}
}
overApproxMdp->addRewardModel("default", mdpRewardModel);
overApproxMdp->restrictRewardModels(std::set<std::string>({"default"}));
}
statistics.overApproximationBuildTime.stop();
STORM_PRINT("Over Approximation MDP build took " << statistics.overApproximationBuildTime << " seconds." << std::endl);
overApproxMdp->printModelInformationToStream(std::cout);
auto modelPtr = std::static_pointer_cast<storm::models::sparse::Model<ValueType, RewardModelType>>(overApproxMdp);
std::string propertyString = computeRewards ? "R" : "P";
propertyString += min ? "min" : "max";
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, false);
auto hint = storm::modelchecker::ExplicitModelCheckerHint<ValueType>();
hint.setResultHint(hintVector);
auto hintPtr = std::make_shared<storm::modelchecker::ExplicitModelCheckerHint<ValueType>>(hint);
task.setHint(hintPtr);
auto property = createStandardProperty(min, computeRewards);
auto task = createStandardCheckTask(property, std::move(hintVector));
statistics.overApproximationCheckTime.start();
std::unique_ptr<storm::modelchecker::CheckResult> res(storm::api::verifyWithSparseEngine<ValueType>(overApproxMdp, task));
statistics.overApproximationCheckTime.stop();
@ -1172,16 +1187,14 @@ namespace storm {
storm::storage::sparse::ModelComponents<ValueType, RewardModelType> modelComponents(mdpTransitionsBuilder.build(mdpMatrixRow, nextMdpStateId, nextMdpStateId), std::move(mdpLabeling));
auto model = std::make_shared<storm::models::sparse::Mdp<ValueType, RewardModelType>>(std::move(modelComponents));
if (computeRewards) {
storm::models::sparse::StandardRewardModel<ValueType> mdpRewardModel(boost::none, std::vector<ValueType>(mdpMatrixRow));
storm::models::sparse::StandardRewardModel<ValueType> mdpRewardModel(boost::none, std::vector<ValueType>(mdpMatrixRow, storm::utility::zero<ValueType>()));
for (auto const &iter : beliefStateMap.left) {
if (fullyExpandedStates.get(iter.second)) {
auto currentBelief = beliefGrid.getGridPoint(iter.first);
auto const& currentBelief = beliefGrid.getGridPoint(iter.first);
auto representativeState = currentBelief.begin()->first;
for (uint64_t action = 0; action < pomdp.getNumberOfChoices(representativeState); ++action) {
// Add the reward
uint64_t mdpChoice = model->getChoiceIndex(storm::storage::StateActionPair(iter.second, action));
uint64_t pomdpChoice = pomdp.getChoiceIndex(storm::storage::StateActionPair(representativeState, action));
mdpRewardModel.setStateActionReward(mdpChoice, getRewardAfterAction(pomdpChoice, currentBelief));
mdpRewardModel.setStateActionReward(mdpChoice, beliefGrid.getBeliefActionReward(currentBelief, action));
}
}
}
@ -1192,17 +1205,11 @@ namespace storm {
model->printModelInformationToStream(std::cout);
statistics.underApproximationBuildTime.stop();
std::string propertyString;
if (computeRewards) {
propertyString = min ? "Rmin=? [F \"target\"]" : "Rmax=? [F \"target\"]";
} else {
propertyString = min ? "Pmin=? [F \"target\"]" : "Pmax=? [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 property = createStandardProperty(min, computeRewards);
auto task = createStandardCheckTask(property, std::vector<ValueType>());
statistics.underApproximationCheckTime.start();
std::unique_ptr<storm::modelchecker::CheckResult> res(storm::api::verifyWithSparseEngine<ValueType>(model, storm::api::createTask<ValueType>(property, false)));
std::unique_ptr<storm::modelchecker::CheckResult> res(storm::api::verifyWithSparseEngine<ValueType>(model, task));
statistics.underApproximationCheckTime.stop();
if (storm::utility::resources::isTerminate() && !res) {
return nullptr;

Write Preview
Loading…
Cancel
Save