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Use the new BeliefMdpExplorer also for the underapproximation.

tempestpy_adaptions
Tim Quatmann 5 years ago
parent
ab26b69435
commit
8b0e582ef4
2 changed files with 55 additions and 115 deletions
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  1. 168
      src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.cpp
  2. 2
      src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.h

168
src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.cpp
View File

@ -456,7 +456,7 @@ namespace storm {
STORM_PRINT("Over-Approximation Result: " << explorer.getComputedValueAtInitialState() << std::endl);
//auto underApprox = weightedSumUnderMap[initialBelief.id];
auto underApproxComponents = computeUnderapproximation(beliefManager, targetObservations, min, computeRewards, maxUaModelSize);
auto underApproxComponents = computeUnderapproximation(beliefManager, targetObservations, min, computeRewards, maxUaModelSize, lowerPomdpValueBounds, upperPomdpValueBounds);
if (storm::utility::resources::isTerminate() && !underApproxComponents) {
// TODO: return other components needed for refinement.
//return std::make_unique<RefinementComponents<ValueType>>(RefinementComponents<ValueType>{modelPtr, overApprox, 0, overApproxResultMap, {}, beliefList, beliefGrid, beliefIsTarget, beliefStateMap, {}, initialBelief.id});
@ -953,91 +953,62 @@ namespace storm {
std::unique_ptr<UnderApproxComponents<ValueType, RewardModelType>>
ApproximatePOMDPModelchecker<ValueType, RewardModelType>::computeUnderapproximation(std::shared_ptr<storm::storage::BeliefManager<storm::models::sparse::Pomdp<ValueType>>> beliefManager,
std::set<uint32_t> const &targetObservations, bool min,
bool computeRewards, uint64_t maxModelSize) {
bool computeRewards, uint64_t maxModelSize, std::vector<ValueType> const& lowerPomdpValueBounds, std::vector<ValueType> const& upperPomdpValueBounds) {
// Build the belief MDP until enough states are explored.
//TODO think of other ways to stop exploration besides model size
statistics.underApproximationBuildTime.start();
// Reserve states 0 and 1 as always sink/goal states
storm::storage::SparseMatrixBuilder<ValueType> mdpTransitionsBuilder(0, 0, 0, true, true);
uint64_t extraBottomState = 0;
uint64_t extraTargetState = computeRewards ? 0 : 1;
uint64_t nextMdpStateId = extraTargetState + 1;
uint64_t mdpMatrixRow = 0;
for (uint64_t state = 0; state < nextMdpStateId; ++state) {
mdpTransitionsBuilder.newRowGroup(mdpMatrixRow);
mdpTransitionsBuilder.addNextValue(mdpMatrixRow, state, storm::utility::one<ValueType>());
++mdpMatrixRow;
storm::builder::BeliefMdpExplorer<storm::models::sparse::Pomdp<ValueType>> explorer(beliefManager, lowerPomdpValueBounds, upperPomdpValueBounds);
if (computeRewards) {
explorer.startNewExploration(storm::utility::zero<ValueType>());
} else {
explorer.startNewExploration(storm::utility::one<ValueType>(), storm::utility::zero<ValueType>());
}
std::vector<uint64_t> targetStates = {extraTargetState};
storm::storage::BitVector fullyExpandedStates;
bsmap_type beliefStateMap;
std::deque<uint64_t> beliefsToBeExpanded;
beliefStateMap.insert(bsmap_type::value_type(beliefManager->getInitialBelief(), nextMdpStateId));
beliefsToBeExpanded.push_back(beliefManager->getInitialBelief());
++nextMdpStateId;
// Expand the believes
storm::storage::BitVector foundBeliefs(beliefManager->getNumberOfBeliefIds(), false);
for (auto const& belId : beliefsToBeExpanded) {
foundBeliefs.set(belId, true);
// Expand the beliefs to generate the grid on-the-fly
if (options.explorationThreshold > storm::utility::zero<ValueType>()) {
STORM_PRINT("Exploration threshold: " << options.explorationThreshold << std::endl)
}
while (!beliefsToBeExpanded.empty()) {
uint64_t currId = beliefsToBeExpanded.front();
beliefsToBeExpanded.pop_front();
uint64_t currMdpState = beliefStateMap.left.at(currId);
auto const& currBelief = beliefManager->getBelief(currId);
uint32_t currObservation = beliefManager->getBeliefObservation(currBelief);
mdpTransitionsBuilder.newRowGroup(mdpMatrixRow);
while (explorer.hasUnexploredState()) {
uint64_t currId = explorer.exploreNextState();
uint32_t currObservation = beliefManager->getBeliefObservation(currId);
if (targetObservations.count(currObservation) != 0) {
// Make this state absorbing
targetStates.push_back(currMdpState);
mdpTransitionsBuilder.addNextValue(mdpMatrixRow, currMdpState, storm::utility::one<ValueType>());
++mdpMatrixRow;
} else if (currMdpState > maxModelSize) {
if (min) {
// Get an upper bound here
if (computeRewards) {
// TODO: With minimizing rewards we need an upper bound!
// In other cases, this could be helpflull as well.
// For now, add a selfloop to "generate" infinite reward
mdpTransitionsBuilder.addNextValue(mdpMatrixRow, currMdpState, storm::utility::one<ValueType>());
} else {
mdpTransitionsBuilder.addNextValue(mdpMatrixRow, extraTargetState, storm::utility::one<ValueType>());
}
} else {
mdpTransitionsBuilder.addNextValue(mdpMatrixRow, computeRewards ? extraTargetState : extraBottomState, storm::utility::one<ValueType>());
}
++mdpMatrixRow;
explorer.setCurrentStateIsTarget();
explorer.addSelfloopTransition();
} else {
fullyExpandedStates.grow(nextMdpStateId, false);
fullyExpandedStates.set(currMdpState, true);
// Iterate over all actions and add the corresponding transitions
uint64_t someState = currBelief.begin()->first;
uint64_t numChoices = pomdp.getNumberOfChoices(someState);
for (uint64_t action = 0; action < numChoices; ++action) {
auto successorBeliefs = beliefManager->expand(currId, action);
// Check for newly found beliefs
foundBeliefs.grow(beliefManager->getNumberOfBeliefIds(), false);
for (auto const& successor : successorBeliefs) {
auto successorId = successor.first;
if (!foundBeliefs.get(successorId)) {
foundBeliefs.set(successorId);
beliefsToBeExpanded.push_back(successorId);
beliefStateMap.insert(bsmap_type::value_type(successorId, nextMdpStateId));
++nextMdpStateId;
bool stopExploration = false;
if (storm::utility::abs<ValueType>(explorer.getUpperValueBoundAtCurrentState() - explorer.getLowerValueBoundAtCurrentState()) < options.explorationThreshold) {
stopExploration = true;
explorer.setCurrentStateIsTruncated();
} else if (explorer.getCurrentNumberOfMdpStates() >= maxModelSize) {
stopExploration = true;
explorer.setCurrentStateIsTruncated();
}
for (uint64 action = 0, numActions = beliefManager->getBeliefNumberOfChoices(currId); action < numActions; ++action) {
ValueType truncationProbability = storm::utility::zero<ValueType>();
ValueType truncationValueBound = storm::utility::zero<ValueType>();
auto successors = beliefManager->expand(currId, action);
for (auto const& successor : successors) {
bool added = explorer.addTransitionToBelief(action, successor.first, successor.second, stopExploration);
if (!added) {
STORM_LOG_ASSERT(stopExploration, "Didn't add a transition although exploration shouldn't be stopped.");
// We did not explore this successor state. Get a bound on the "missing" value
truncationProbability += successor.second;
truncationValueBound += successor.second * (min ? explorer.computeUpperValueBoundAtBelief(successor.first) : explorer.computeLowerValueBoundAtBelief(successor.first));
}
auto successorMdpState = beliefStateMap.left.at(successorId);
// This assumes that the successor MDP states are given in ascending order, which is indeed the case because the successorBeliefs are sorted.
mdpTransitionsBuilder.addNextValue(mdpMatrixRow, successorMdpState, successor.second);
}
++mdpMatrixRow;
if (stopExploration) {
if (computeRewards) {
explorer.addTransitionsToExtraStates(action, truncationProbability);
} else {
explorer.addTransitionsToExtraStates(action, truncationValueBound, truncationProbability - truncationValueBound);
}
}
if (computeRewards) {
// The truncationValueBound will be added on top of the reward introduced by the current belief state.
explorer.computeRewardAtCurrentState(action, truncationValueBound);
}
}
}
if (storm::utility::resources::isTerminate()) {
@ -1045,56 +1016,25 @@ namespace storm {
break;
}
}
statistics.underApproximationStates = nextMdpStateId;
statistics.underApproximationStates = explorer.getCurrentNumberOfMdpStates();
if (storm::utility::resources::isTerminate()) {
statistics.underApproximationBuildTime.stop();
return nullptr;
}
fullyExpandedStates.resize(nextMdpStateId, false);
storm::models::sparse::StateLabeling mdpLabeling(nextMdpStateId);
mdpLabeling.addLabel("init");
mdpLabeling.addLabel("target");
mdpLabeling.addLabelToState("init", beliefStateMap.left.at(beliefManager->getInitialBelief()));
for (auto targetState : targetStates) {
mdpLabeling.addLabelToState("target", targetState);
}
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::utility::zero<ValueType>()));
for (auto const &iter : beliefStateMap.left) {
if (fullyExpandedStates.get(iter.second)) {
auto const& currentBelief = beliefManager->getBelief(iter.first);
auto representativeState = currentBelief.begin()->first;
for (uint64_t action = 0; action < pomdp.getNumberOfChoices(representativeState); ++action) {
uint64_t mdpChoice = model->getChoiceIndex(storm::storage::StateActionPair(iter.second, action));
mdpRewardModel.setStateActionReward(mdpChoice, beliefManager->getBeliefActionReward(iter.first, action));
}
}
}
model->addRewardModel("default", mdpRewardModel);
model->restrictRewardModels(std::set<std::string>({"default"}));
}
model->printModelInformationToStream(std::cout);
explorer.finishExploration();
statistics.underApproximationBuildTime.stop();
STORM_PRINT("Under Approximation MDP build took " << statistics.underApproximationBuildTime << " seconds." << std::endl);
explorer.getExploredMdp()->printModelInformationToStream(std::cout);
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, task));
explorer.computeValuesOfExploredMdp(min ? storm::solver::OptimizationDirection::Minimize : storm::solver::OptimizationDirection::Maximize);
statistics.underApproximationCheckTime.stop();
if (storm::utility::resources::isTerminate() && !res) {
return nullptr;
}
STORM_LOG_ASSERT(res, "Result does not exist.");
res->filter(storm::modelchecker::ExplicitQualitativeCheckResult(storm::storage::BitVector(model->getNumberOfStates(), true)));
auto underApproxResultMap = res->asExplicitQuantitativeCheckResult<ValueType>().getValueMap();
auto underApprox = underApproxResultMap[beliefStateMap.left.at(beliefManager->getInitialBelief())];
return std::make_unique<UnderApproxComponents<ValueType>>(UnderApproxComponents<ValueType>{underApprox, underApproxResultMap, beliefStateMap});
STORM_PRINT("Time Underapproximation: " << statistics.underApproximationCheckTime << " seconds." << std::endl);
STORM_PRINT("Under-Approximation Result: " << explorer.getComputedValueAtInitialState() << std::endl);
return std::make_unique<UnderApproxComponents<ValueType>>(UnderApproxComponents<ValueType>{explorer.getComputedValueAtInitialState(), {}, {}});
}

2
src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.h
View File

@ -162,7 +162,7 @@ namespace storm {
uint64_t maxModelSize);
std::unique_ptr<UnderApproxComponents<ValueType, RewardModelType>> computeUnderapproximation(std::shared_ptr<storm::storage::BeliefManager<storm::models::sparse::Pomdp<ValueType>>> beliefManager,
std::set<uint32_t> const &targetObservations, bool min, bool computeReward,
uint64_t maxModelSize);
uint64_t maxModelSize, std::vector<ValueType> const& lowerPomdpValueBounds, std::vector<ValueType> const& upperPomdpValueBounds);
/**
* Constructs the initial belief for the given POMDP

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