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Added reward MDP generation for the overapproximation

main
Alexander Bork 6 years ago
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c663edbd85
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c6902e0ca7
1 changed files with 96 additions and 18 deletions
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  1. 114
      src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.cpp

114
src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.cpp
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@ -78,8 +78,7 @@ namespace storm {
storm::pomdp::Belief<ValueType> initialBelief = getInitialBelief(pomdp, nextId);
++nextId;
beliefList.push_back(initialBelief);
beliefIsTarget.push_back(
targetObservations.find(initialBelief.observation) != targetObservations.end());
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;
@ -344,8 +343,6 @@ namespace storm {
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);
@ -398,8 +395,16 @@ namespace storm {
storm::pomdp::Belief<ValueType> initialBelief = getInitialBelief(pomdp, nextId);
++nextId;
beliefList.push_back(initialBelief);
beliefIsTarget.push_back(
targetObservations.find(initialBelief.observation) != targetObservations.end());
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(
@ -410,6 +415,8 @@ namespace storm {
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>())) {
@ -429,11 +436,22 @@ namespace storm {
targetObservations.find(initialBelief.observation) != targetObservations.end());
beliefsToBeExpanded.push_back(nextId);
++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
@ -445,17 +463,26 @@ namespace storm {
result.emplace(std::make_pair(currId, storm::utility::zero<ValueType>()));
result_backup.emplace(std::make_pair(currId, storm::utility::zero<ValueType>()));
if (!isTarget) {
if (isTarget) {
// 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 {
uint64_t representativeState = pomdp.getStatesWithObservation(beliefList[currId].observation).front();
uint64_t numChoices = pomdp.getNumberOfChoices(representativeState);
std::vector<std::map<uint32_t, ValueType>> observationProbabilitiesInAction(numChoices);
std::vector<std::map<uint32_t, uint64_t>> nextBelievesInAction(numChoices);
std::vector<ValueType> actionRewardsInState(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
@ -490,7 +517,13 @@ namespace storm {
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];
}
}
}
@ -499,12 +532,20 @@ namespace storm {
nextBelievesInAction[action] = actionObservationBelieves;
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));
observationProbabilities.emplace(std::make_pair(currId, observationProbabilitiesInAction));
nextBelieves.emplace(std::make_pair(currId, nextBelievesInAction));
beliefActionRewards.emplace(std::make_pair(currId, actionRewardsInState));
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();
@ -512,6 +553,31 @@ namespace storm {
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);
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.printModelInformationToStream(std::cout);
storm::utility::Stopwatch overApproxTimer(true);
// Value Iteration
while (!finished && iteration < maxIterations) {
@ -600,13 +666,12 @@ namespace storm {
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])];
overApprox += initLambdas[j] * result_backup[getBeliefIdInVector(beliefGrid, initialBelief.observation, initSubSimplex[j])];
}
}
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, true);
@ -615,13 +680,26 @@ namespace storm {
STORM_PRINT("Time Overapproximation: " << overApproxTimer
<< std::endl
<< "Time Underapproximation: " << underApproxTimer
<< std::endl);
<< std::endl);*/
STORM_PRINT("Over-Approximation Result: " << overApprox << std::endl);
STORM_PRINT("Under-Approximation Result: " << underApprox << std::endl);
//STORM_PRINT("Under-Approximation Result: " << underApprox << std::endl);
return std::make_unique<POMDPCheckResult<ValueType>>(
POMDPCheckResult<ValueType>{overApprox, underApprox});
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, "rewardTest", parameterNames);
std::string propertyString = min ? "Rmin=? [F \"target\"]" : "Rmax=? [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});
}

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