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Refactoring of on-the-fly computation to reduce code duplication

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Alexander Bork 6 years ago
parent
c6902e0ca7
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4664b4244b
2 changed files with 73 additions and 352 deletions
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  1. 410
      src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.cpp
  2. 15
      src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.h

410
src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.cpp
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@ -22,6 +22,7 @@ namespace storm {
precision = 0.000000001;
cc = storm::utility::ConstantsComparator<ValueType>(storm::utility::convertNumber<ValueType>(precision), false);
useMdp = false;
maxIterations = 1000;
}
template<typename ValueType, typename RewardModelType>
@ -47,327 +48,15 @@ namespace storm {
template<typename ValueType, typename RewardModelType>
std::unique_ptr<POMDPCheckResult<ValueType>>
ApproximatePOMDPModelchecker<ValueType, RewardModelType>::computeReachabilityProbabilityOTF(storm::models::sparse::Pomdp<ValueType, RewardModelType> const &pomdp,
std::set<uint32_t> targetObservations, bool min, uint64_t gridResolution) {
ApproximatePOMDPModelchecker<ValueType, RewardModelType>::computeReachabilityOTF(storm::models::sparse::Pomdp<ValueType, RewardModelType> const &pomdp,
std::set<uint32_t> targetObservations, bool min, uint64_t gridResolution,
bool computeRewards) {
STORM_PRINT("Use On-The-Fly Grid Generation" << std::endl)
uint64_t maxIterations = 100;
bool finished = false;
uint64_t iteration = 0;
std::vector<storm::pomdp::Belief<ValueType>> beliefList;
std::vector<bool> beliefIsTarget;
std::vector<storm::pomdp::Belief<ValueType>> beliefGrid;
std::map<uint64_t, ValueType> result;
std::map<uint64_t, ValueType> result_backup;
//Use caching to avoid multiple computation of the subsimplices and lambdas
std::map<uint64_t, std::vector<std::vector<ValueType>>> subSimplexCache;
std::map<uint64_t, std::vector<ValueType>> lambdaCache;
std::map<uint64_t, std::vector<uint64_t>> chosenActions;
std::deque<uint64_t> beliefsToBeExpanded;
// Belief ID -> Observation -> Probability
std::map<uint64_t, std::vector<std::map<uint32_t, ValueType>>> observationProbabilities;
// current ID -> action -> next ID
std::map<uint64_t, std::vector<std::map<uint32_t, uint64_t>>> nextBelieves;
uint64_t nextId = 0;
storm::utility::Stopwatch expansionTimer(true);
// Initial belief always has ID 0
storm::pomdp::Belief<ValueType> initialBelief = getInitialBelief(pomdp, nextId);
++nextId;
beliefList.push_back(initialBelief);
beliefIsTarget.push_back(targetObservations.find(initialBelief.observation) != targetObservations.end());
// These are the components to build the MDP from the grid
std::map<uint64_t, uint64_t> beliefStateMap;
std::vector<std::vector<std::map<uint64_t, ValueType>>> mdpTransitions;
std::vector<uint64_t> targetStates;
uint64_t mdpStateId = 0;
beliefStateMap[initialBelief.id] = mdpStateId;
++mdpStateId;
// for the initial belief, add the triangulated initial states
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;
subSimplexCache[0] = initSubSimplex;
lambdaCache[0] = initLambdas;
bool initInserted = false;
std::vector<std::map<uint64_t, ValueType>> initTransitionsInBelief;
std::map<uint64_t, ValueType> initTransitionInActionBelief;
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]);
if (searchResult == uint64_t(-1) || (searchResult == 0 && !initInserted)) {
if (searchResult == 0) {
// the initial belief is on the grid itself
initInserted = true;
beliefGrid.push_back(initialBelief);
beliefsToBeExpanded.push_back(0);
} else {
// if the triangulated belief was not found in the list, we place it in the grid and add it to the work list
storm::pomdp::Belief<ValueType> gridBelief = {nextId, initialBelief.observation, initSubSimplex[j]};
beliefList.push_back(gridBelief);
beliefGrid.push_back(gridBelief);
beliefIsTarget.push_back(targetObservations.find(initialBelief.observation) != targetObservations.end());
beliefsToBeExpanded.push_back(nextId);
beliefStateMap[nextId] = mdpStateId;
initTransitionInActionBelief[mdpStateId] = initLambdas[j];
++nextId;
++mdpStateId;
}
}
}
}
// If the initial belief is not on the grid, we add the transitions from our initial MDP state to the triangulated beliefs
if (!initTransitionInActionBelief.empty()) {
initTransitionsInBelief.push_back(initTransitionInActionBelief);
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
// Expand the beliefs to generate the grid on-the-fly to avoid unreachable grid points
while (!beliefsToBeExpanded.empty()) {
uint64_t currId = beliefsToBeExpanded.front();
beliefsToBeExpanded.pop_front();
bool isTarget = beliefIsTarget[currId];
if (isTarget) {
result.emplace(std::make_pair(currId, storm::utility::one<ValueType>()));
result_backup.emplace(std::make_pair(currId, storm::utility::one<ValueType>()));
// MDP stuff
std::vector<std::map<uint64_t, ValueType>> transitionsInBelief;
targetStates.push_back(beliefStateMap[currId]);
std::map<uint64_t, ValueType> transitionInActionBelief;
transitionInActionBelief[beliefStateMap[currId]] = storm::utility::one<ValueType>();
transitionsInBelief.push_back(transitionInActionBelief);
mdpTransitions.push_back(transitionsInBelief);
} else {
result.emplace(std::make_pair(currId, storm::utility::zero<ValueType>()));
result_backup.emplace(std::make_pair(currId, storm::utility::zero<ValueType>()));
uint64_t numChoices = pomdp.getNumberOfChoices(pomdp.getStatesWithObservation(beliefList[currId].observation).front());
std::vector<std::map<uint32_t, ValueType>> observationProbabilitiesInAction(numChoices);
std::vector<std::map<uint32_t, uint64_t>> nextBelievesInAction(numChoices);
std::vector<std::map<uint64_t, ValueType>> transitionsInBelief;
for (uint64_t action = 0; action < numChoices; ++action) {
std::map<uint32_t, ValueType> actionObservationProbabilities = computeObservationProbabilitiesAfterAction(pomdp, beliefList[currId], action);
std::map<uint32_t, uint64_t> actionObservationBelieves;
std::map<uint64_t, ValueType> transitionInActionBelief;
for (auto iter = actionObservationProbabilities.begin(); iter != actionObservationProbabilities.end(); ++iter) {
uint32_t observation = iter->first;
// THIS CALL IS SLOW
// TODO speed this up
uint64_t idNextBelief = getBeliefAfterActionAndObservation(pomdp, beliefList, beliefIsTarget, targetObservations, beliefList[currId], action,
observation, nextId);
nextId = beliefList.size();
actionObservationBelieves[observation] = idNextBelief;
//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
subSimplex = subSimplexCache[idNextBelief];
lambdas = lambdaCache[idNextBelief];
} else {
std::pair<std::vector<std::vector<ValueType>>, std::vector<ValueType>> temp = computeSubSimplexAndLambdas(
beliefList[idNextBelief].probabilities, gridResolution);
subSimplex = temp.first;
lambdas = temp.second;
subSimplexCache[idNextBelief] = subSimplex;
lambdaCache[idNextBelief] = lambdas;
}
for (size_t j = 0; j < lambdas.size(); ++j) {
if (!cc.isEqual(lambdas[j], storm::utility::zero<ValueType>())) {
if (getBeliefIdInVector(beliefGrid, observation, subSimplex[j]) == uint64_t(-1)) {
// if the triangulated belief was not found in the list, we place it in the grid and add it to the work list
storm::pomdp::Belief<ValueType> gridBelief = {nextId, observation, subSimplex[j]};
beliefList.push_back(gridBelief);
beliefGrid.push_back(gridBelief);
beliefIsTarget.push_back(targetObservations.find(observation) != targetObservations.end());
beliefsToBeExpanded.push_back(nextId);
beliefStateMap[nextId] = mdpStateId;
transitionInActionBelief[mdpStateId] = iter->second * lambdas[j];
++nextId;
++mdpStateId;
} else {
transitionInActionBelief[beliefStateMap[getBeliefIdInVector(beliefGrid, observation, subSimplex[j])]] = iter->second * lambdas[j];
}
}
}
}
observationProbabilitiesInAction[action] = actionObservationProbabilities;
nextBelievesInAction[action] = actionObservationBelieves;
if (!transitionInActionBelief.empty()) {
transitionsInBelief.push_back(transitionInActionBelief);
}
}
observationProbabilities.emplace(std::make_pair(currId, observationProbabilitiesInAction));
nextBelieves.emplace(std::make_pair(currId, nextBelievesInAction));
if (transitionsInBelief.empty()) {
std::map<uint64_t, ValueType> transitionInActionBelief;
transitionInActionBelief[beliefStateMap[currId]] = storm::utility::one<ValueType>();
transitionsInBelief.push_back(transitionInActionBelief);
}
mdpTransitions.push_back(transitionsInBelief);
}
}
expansionTimer.stop();
STORM_PRINT("Grid size: " << beliefGrid.size() << std::endl)
STORM_PRINT("#Believes in List: " << beliefList.size() << std::endl)
STORM_PRINT("Belief space expansion took " << expansionTimer << std::endl)
storm::models::sparse::StateLabeling mdpLabeling(mdpTransitions.size());
mdpLabeling.addLabel("init");
mdpLabeling.addLabel("target");
mdpLabeling.addLabelToState("init", 0);
for (auto targetState : targetStates) {
mdpLabeling.addLabelToState("target", targetState);
}
storm::storage::sparse::ModelComponents<ValueType, RewardModelType> modelComponents(buildTransitionMatrix(mdpTransitions), mdpLabeling);
storm::models::sparse::Mdp<ValueType, RewardModelType> overApproxMdp(modelComponents);
overApproxMdp.printModelInformationToStream(std::cout);
storm::utility::Stopwatch overApproxTimer(true);
// Value Iteration
while (!finished && iteration < maxIterations) {
storm::utility::Stopwatch iterationTimer(true);
STORM_LOG_DEBUG("Iteration " << iteration + 1);
bool improvement = false;
for (size_t i = 0; i < beliefGrid.size(); ++i) {
storm::pomdp::Belief<ValueType> currentBelief = beliefGrid[i];
bool isTarget = beliefIsTarget[currentBelief.id];
if (!isTarget) {
// we can take any state with the observation as they have the same number of choices
uint64_t numChoices = pomdp.getNumberOfChoices(
pomdp.getStatesWithObservation(currentBelief.observation).front());
// Initialize the values for the value iteration
ValueType chosenValue = min ? storm::utility::infinity<ValueType>() : -storm::utility::infinity<ValueType>();
std::vector<uint64_t> chosenActionIndices;
ValueType currentValue;
for (uint64_t action = 0; action < numChoices; ++action) {
currentValue = storm::utility::zero<ValueType>();
for (auto iter = observationProbabilities[currentBelief.id][action].begin();
iter != observationProbabilities[currentBelief.id][action].end(); ++iter) {
uint32_t observation = iter->first;
storm::pomdp::Belief<ValueType> nextBelief = beliefList[nextBelieves[currentBelief.id][action][observation]];
// compute subsimplex and lambdas according to the Lovejoy paper to approximate the next belief
// cache the values to not always re-calculate
std::vector<std::vector<ValueType>> subSimplex;
std::vector<ValueType> lambdas;
if (subSimplexCache.count(nextBelief.id) > 0) {
subSimplex = subSimplexCache[nextBelief.id];
lambdas = lambdaCache[nextBelief.id];
} else {
// TODO is this necessary here? Everything should have already been computed
std::pair<std::vector<std::vector<ValueType>>, std::vector<ValueType>> temp = computeSubSimplexAndLambdas(
nextBelief.probabilities, gridResolution);
subSimplex = temp.first;
lambdas = temp.second;
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>())) {
sum += lambdas[j] * result_backup.at(getBeliefIdInVector(beliefGrid, observation, subSimplex[j]));
}
}
currentValue += iter->second * sum;
}
// Update the selected actions
if ((min && cc.isLess(storm::utility::zero<ValueType>(), chosenValue - currentValue)) ||
(!min &&
cc.isLess(storm::utility::zero<ValueType>(), currentValue - chosenValue)) ||
cc.isEqual(storm::utility::zero<ValueType>(), chosenValue - currentValue)) {
chosenValue = currentValue;
if (!(useMdp && cc.isEqual(storm::utility::zero<ValueType>(), chosenValue - currentValue))) {
chosenActionIndices.clear();
}
chosenActionIndices.push_back(action);
}
}
result[currentBelief.id] = chosenValue;
chosenActions[currentBelief.id] = chosenActionIndices;
// Check if the iteration brought an improvement
if (cc.isLess(storm::utility::zero<ValueType>(), result[currentBelief.id] - result_backup[currentBelief.id])) {
improvement = true;
}
}
}
finished = !improvement;
// back up
result_backup = result;
++iteration;
iterationTimer.stop();
STORM_PRINT("Iteration " << iteration << ": " << iterationTimer << std::endl);
}
STORM_PRINT("Overapproximation took " << iteration << " iterations" << std::endl);
auto overApprox = storm::utility::zero<ValueType>();
for (size_t j = 0; j < initLambdas.size(); ++j) {
if (initLambdas[j] != storm::utility::zero<ValueType>()) {
overApprox += initLambdas[j] *
result_backup[getBeliefIdInVector(beliefGrid, initialBelief.observation,
initSubSimplex[j])];
}
if (computeRewards) {
RewardModelType const &pomdpRewardModel = pomdp.getUniqueRewardModel();
}
overApproxTimer.stop();
ValueType underApprox = storm::utility::zero<ValueType>();
/*storm::utility::Stopwatch underApproxTimer(true);
ValueType underApprox = computeUnderapproximationWithMDP(pomdp, beliefList, beliefIsTarget, targetObservations, observationProbabilities, nextBelieves,
result, chosenActions, gridResolution, initialBelief.id, min, false);
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;
storm::api::exportSparseModelAsDrn(modelPtr, "test", parameterNames);
std::string 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();
std::unique_ptr<storm::modelchecker::CheckResult> res(storm::api::verifyWithSparseEngine<ValueType>(model, storm::api::createTask<ValueType>(property, true)));
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);
return std::make_unique<POMDPCheckResult<ValueType>>(POMDPCheckResult<ValueType>{overApprox, underApprox});
}
template<typename ValueType, typename RewardModelType>
std::unique_ptr<POMDPCheckResult<ValueType>>
ApproximatePOMDPModelchecker<ValueType, RewardModelType>::computeReachabilityRewardOTF(storm::models::sparse::Pomdp<ValueType, RewardModelType> const &pomdp,
std::set<uint32_t> targetObservations, bool min, uint64_t gridResolution) {
STORM_PRINT("Use On-The-Fly Grid Generation" << std::endl)
RewardModelType pomdpRewardModel = pomdp.getUniqueRewardModel();
uint64_t maxIterations = 100;
bool finished = false;
uint64_t iteration = 0;
std::vector<storm::pomdp::Belief<ValueType>> beliefList;
@ -407,8 +96,7 @@ namespace storm {
++mdpStateId;
// for the initial belief, add the triangulated initial states
std::pair<std::vector<std::vector<ValueType>>, std::vector<ValueType>> initTemp = computeSubSimplexAndLambdas(
initialBelief.probabilities, gridResolution);
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;
subSimplexCache[0] = initSubSimplex;
@ -432,8 +120,7 @@ namespace storm {
storm::pomdp::Belief<ValueType> gridBelief = {nextId, initialBelief.observation, initSubSimplex[j]};
beliefList.push_back(gridBelief);
beliefGrid.push_back(gridBelief);
beliefIsTarget.push_back(
targetObservations.find(initialBelief.observation) != targetObservations.end());
beliefIsTarget.push_back(targetObservations.find(initialBelief.observation) != targetObservations.end());
beliefsToBeExpanded.push_back(nextId);
++nextId;
@ -461,9 +148,11 @@ namespace storm {
beliefsToBeExpanded.pop_front();
bool isTarget = beliefIsTarget[currId];
result.emplace(std::make_pair(currId, storm::utility::zero<ValueType>()));
result_backup.emplace(std::make_pair(currId, storm::utility::zero<ValueType>()));
if (isTarget) {
// Depending on whether we compute rewards, we select the right initial result
result.emplace(std::make_pair(currId, computeRewards ? storm::utility::zero<ValueType>() : storm::utility::one<ValueType>()));
result_backup.emplace(std::make_pair(currId, computeRewards ? storm::utility::zero<ValueType>() : storm::utility::one<ValueType>()));
// MDP stuff
std::vector<std::map<uint64_t, ValueType>> transitionsInBelief;
targetStates.push_back(beliefStateMap[currId]);
@ -472,6 +161,9 @@ namespace storm {
transitionsInBelief.push_back(transitionInActionBelief);
mdpTransitions.push_back(transitionsInBelief);
} else {
result.emplace(std::make_pair(currId, storm::utility::zero<ValueType>()));
result_backup.emplace(std::make_pair(currId, storm::utility::zero<ValueType>()));
uint64_t representativeState = pomdp.getStatesWithObservation(beliefList[currId].observation).front();
uint64_t numChoices = pomdp.getNumberOfChoices(representativeState);
std::vector<std::map<uint32_t, ValueType>> observationProbabilitiesInAction(numChoices);
@ -530,15 +222,20 @@ namespace storm {
}
observationProbabilitiesInAction[action] = actionObservationProbabilities;
nextBelievesInAction[action] = actionObservationBelieves;
actionRewardsInState[action] = getRewardAfterAction(pomdp, pomdp.getChoiceIndex(storm::storage::StateActionPair(representativeState, action)),
beliefList[currId]);
if (computeRewards) {
actionRewardsInState[action] = getRewardAfterAction(pomdp, pomdp.getChoiceIndex(storm::storage::StateActionPair(representativeState, action)),
beliefList[currId]);
}
if (!transitionInActionBelief.empty()) {
transitionsInBelief.push_back(transitionInActionBelief);
}
}
observationProbabilities.emplace(std::make_pair(currId, observationProbabilitiesInAction));
nextBelieves.emplace(std::make_pair(currId, nextBelievesInAction));
beliefActionRewards.emplace(std::make_pair(currId, actionRewardsInState));
if (computeRewards) {
beliefActionRewards.emplace(std::make_pair(currId, actionRewardsInState));
}
if (transitionsInBelief.empty()) {
std::map<uint64_t, ValueType> transitionInActionBelief;
@ -563,19 +260,21 @@ namespace storm {
storm::storage::sparse::ModelComponents<ValueType, RewardModelType> modelComponents(buildTransitionMatrix(mdpTransitions), mdpLabeling);
storm::models::sparse::Mdp<ValueType, RewardModelType> overApproxMdp(modelComponents);
storm::models::sparse::StandardRewardModel<ValueType> mdpRewardModel(boost::none, std::vector<ValueType>(modelComponents.transitionMatrix.getRowCount()));
for (auto const &iter : beliefStateMap) {
auto currentBelief = beliefList[iter.first];
auto representativeState = pomdp.getStatesWithObservation(currentBelief.observation).front();
for (uint64_t action = 0; action < overApproxMdp.getNumberOfChoices(iter.second); ++action) {
// Add the reward
mdpRewardModel.setStateActionReward(overApproxMdp.getChoiceIndex(storm::storage::StateActionPair(iter.second, action)),
getRewardAfterAction(pomdp, pomdp.getChoiceIndex(storm::storage::StateActionPair(representativeState, action)),
currentBelief));
if (computeRewards) {
storm::models::sparse::StandardRewardModel<ValueType> mdpRewardModel(boost::none, std::vector<ValueType>(modelComponents.transitionMatrix.getRowCount()));
for (auto const &iter : beliefStateMap) {
auto currentBelief = beliefList[iter.first];
auto representativeState = pomdp.getStatesWithObservation(currentBelief.observation).front();
for (uint64_t action = 0; action < overApproxMdp.getNumberOfChoices(iter.second); ++action) {
// Add the reward
mdpRewardModel.setStateActionReward(overApproxMdp.getChoiceIndex(storm::storage::StateActionPair(iter.second, action)),
getRewardAfterAction(pomdp, pomdp.getChoiceIndex(storm::storage::StateActionPair(representativeState, action)),
currentBelief));
}
}
overApproxMdp.addRewardModel("std", mdpRewardModel);
overApproxMdp.restrictRewardModels(std::set<std::string>({"std"}));
}
overApproxMdp.addRewardModel("std", mdpRewardModel);
overApproxMdp.restrictRewardModels(std::set<std::string>({"std"}));
overApproxMdp.printModelInformationToStream(std::cout);
storm::utility::Stopwatch overApproxTimer(true);
@ -589,16 +288,14 @@ namespace storm {
bool isTarget = beliefIsTarget[currentBelief.id];
if (!isTarget) {
// we can take any state with the observation as they have the same number of choices
uint64_t numChoices = pomdp.getNumberOfChoices(
pomdp.getStatesWithObservation(currentBelief.observation).front());
uint64_t numChoices = pomdp.getNumberOfChoices(pomdp.getStatesWithObservation(currentBelief.observation).front());
// Initialize the values for the value iteration
ValueType chosenValue = min ? storm::utility::infinity<ValueType>()
: -storm::utility::infinity<ValueType>();
ValueType chosenValue = min ? storm::utility::infinity<ValueType>() : -storm::utility::infinity<ValueType>();
std::vector<uint64_t> chosenActionIndices;
ValueType currentValue;
for (uint64_t action = 0; action < numChoices; ++action) {
currentValue = beliefActionRewards[currentBelief.id][action];
currentValue = computeRewards ? beliefActionRewards[currentBelief.id][action] : storm::utility::zero<ValueType>();
for (auto iter = observationProbabilities[currentBelief.id][action].begin();
iter != observationProbabilities[currentBelief.id][action].end(); ++iter) {
uint32_t observation = iter->first;
@ -622,8 +319,7 @@ namespace storm {
auto sum = storm::utility::zero<ValueType>();
for (size_t j = 0; j < lambdas.size(); ++j) {
if (!cc.isEqual(lambdas[j], storm::utility::zero<ValueType>())) {
sum += lambdas[j] * result_backup.at(
getBeliefIdInVector(beliefGrid, observation, subSimplex[j]));
sum += lambdas[j] * result_backup.at(getBeliefIdInVector(beliefGrid, observation, subSimplex[j]));
}
}
@ -631,8 +327,7 @@ namespace storm {
}
// Update the selected actions
if ((min && cc.isLess(storm::utility::zero<ValueType>(), chosenValue - currentValue)) ||
(!min &&
cc.isLess(storm::utility::zero<ValueType>(), currentValue - chosenValue)) ||
(!min && cc.isLess(storm::utility::zero<ValueType>(), currentValue - chosenValue)) ||
cc.isEqual(storm::utility::zero<ValueType>(), chosenValue - currentValue)) {
chosenValue = currentValue;
if (!(useMdp && cc.isEqual(storm::utility::zero<ValueType>(), chosenValue - currentValue))) {
@ -646,8 +341,7 @@ namespace storm {
chosenActions[currentBelief.id] = chosenActionIndices;
// Check if the iteration brought an improvement
if (cc.isLess(storm::utility::zero<ValueType>(), result_backup[currentBelief.id] - result[currentBelief.id]) ||
cc.isLess(storm::utility::zero<ValueType>(), result[currentBelief.id] - result_backup[currentBelief.id])) {
if (!cc.isEqual(result_backup[currentBelief.id], result[currentBelief.id])) {
improvement = true;
}
}
@ -674,7 +368,7 @@ namespace storm {
/*
storm::utility::Stopwatch underApproxTimer(true);
ValueType underApprox = computeUnderapproximationWithMDP(pomdp, beliefList, beliefIsTarget, targetObservations, observationProbabilities, nextBelieves,
result, chosenActions, gridResolution, initialBelief.id, min, true);
result, chosenActions, gridResolution, initialBelief.id, min, computeRewards);
underApproxTimer.stop();
STORM_PRINT("Time Overapproximation: " << overApproxTimer
@ -690,7 +384,9 @@ namespace storm {
std::vector<std::string> parameterNames;
storm::api::exportSparseModelAsDrn(modelPtr, "rewardTest", parameterNames);
std::string propertyString = min ? "Rmin=? [F \"target\"]" : "Rmax=? [F \"target\"]";
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();
@ -700,16 +396,27 @@ namespace storm {
STORM_PRINT("OverApprox MDP: " << (res->asExplicitQuantitativeCheckResult<ValueType>().getValueMap().begin()->second) << std::endl);
return std::make_unique<POMDPCheckResult<ValueType>>(POMDPCheckResult<ValueType>{overApprox, underApprox});
}
template<typename ValueType, typename RewardModelType>
std::unique_ptr<POMDPCheckResult<ValueType>>
ApproximatePOMDPModelchecker<ValueType, RewardModelType>::computeReachabilityRewardOTF(storm::models::sparse::Pomdp<ValueType, RewardModelType> const &pomdp,
std::set<uint32_t> targetObservations, bool min, uint64_t gridResolution) {
return computeReachabilityOTF(pomdp, targetObservations, min, gridResolution, true);
}
template<typename ValueType, typename RewardModelType>
std::unique_ptr<POMDPCheckResult<ValueType>>
ApproximatePOMDPModelchecker<ValueType, RewardModelType>::computeReachabilityProbabilityOTF(storm::models::sparse::Pomdp<ValueType, RewardModelType> const &pomdp,
std::set<uint32_t> targetObservations, bool min, uint64_t gridResolution) {
return computeReachabilityOTF(pomdp, targetObservations, min, gridResolution, false);
}
template<typename ValueType, typename RewardModelType>
std::unique_ptr<POMDPCheckResult<ValueType>>
ApproximatePOMDPModelchecker<ValueType, RewardModelType>::computeReachabilityProbability(storm::models::sparse::Pomdp<ValueType, RewardModelType> const &pomdp,
std::set<uint32_t> targetObservations, bool min, uint64_t gridResolution) {
storm::utility::Stopwatch beliefGridTimer(true);
uint64_t maxIterations = 100;
bool finished = false;
uint64_t iteration = 0;
@ -915,7 +622,6 @@ namespace storm {
ApproximatePOMDPModelchecker<ValueType, RewardModelType>::computeReachabilityReward(storm::models::sparse::Pomdp<ValueType, RewardModelType> const &pomdp,
std::set<uint32_t> targetObservations, bool min, uint64_t gridResolution) {
storm::utility::Stopwatch beliefGridTimer(true);
uint64_t maxIterations = 100;
bool finished = false;
uint64_t iteration = 0;

15
src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.h
View File

@ -45,6 +45,20 @@ namespace storm {
uint64_t gridResolution);
private:
/**
*
* @param pomdp
* @param targetObservations
* @param min
* @param gridResolution
* @param computeRewards
* @return
*/
std::unique_ptr<POMDPCheckResult<ValueType>>
computeReachabilityOTF(storm::models::sparse::Pomdp<ValueType, RewardModelType> const &pomdp,
std::set<uint32_t> targetObservations, bool min,
uint64_t gridResolution, bool computeRewards);
/**
* TODO
* @param pomdp
@ -225,6 +239,7 @@ namespace storm {
storm::utility::ConstantsComparator<ValueType> cc;
double precision;
bool useMdp;
uint64_t maxIterations;
};
}

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