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Refactoring of reachability reward and probability methods to reduce code duplication

tempestpy_adaptions
Alexander Bork 5 years ago
parent
4664b4244b
commit
8f81958268
2 changed files with 66 additions and 258 deletions
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  1. 310
      src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.cpp
  2. 14
      src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.h

310
src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.cpp
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@ -414,12 +414,17 @@ namespace storm {
template<typename ValueType, typename RewardModelType> template<typename ValueType, typename RewardModelType>
std::unique_ptr<POMDPCheckResult<ValueType>> 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) {
ApproximatePOMDPModelchecker<ValueType, RewardModelType>::computeReachability(storm::models::sparse::Pomdp<ValueType, RewardModelType> const &pomdp,
std::set<uint32_t> targetObservations, bool min, uint64_t gridResolution,
bool computeRewards) {
storm::utility::Stopwatch beliefGridTimer(true); storm::utility::Stopwatch beliefGridTimer(true);
bool finished = false; bool finished = false;
uint64_t iteration = 0; uint64_t iteration = 0;
if (computeRewards) {
RewardModelType pomdpRewardModel = pomdp.getUniqueRewardModel();
}
std::vector<storm::pomdp::Belief<ValueType>> beliefList; std::vector<storm::pomdp::Belief<ValueType>> beliefList;
std::vector<bool> beliefIsTarget; std::vector<bool> beliefIsTarget;
uint64_t nextId = 0; uint64_t nextId = 0;
@ -427,13 +432,10 @@ namespace storm {
storm::pomdp::Belief<ValueType> initialBelief = getInitialBelief(pomdp, nextId); storm::pomdp::Belief<ValueType> initialBelief = getInitialBelief(pomdp, nextId);
++nextId; ++nextId;
beliefList.push_back(initialBelief); beliefList.push_back(initialBelief);
beliefIsTarget.push_back(
targetObservations.find(initialBelief.observation) != targetObservations.end());
beliefIsTarget.push_back(targetObservations.find(initialBelief.observation) != targetObservations.end());
std::vector<storm::pomdp::Belief<ValueType>> beliefGrid; std::vector<storm::pomdp::Belief<ValueType>> beliefGrid;
constructBeliefGrid(pomdp, targetObservations, gridResolution, beliefList, beliefGrid, beliefIsTarget,
nextId);
constructBeliefGrid(pomdp, targetObservations, gridResolution, beliefList, beliefGrid, beliefIsTarget, nextId);
nextId = beliefList.size(); nextId = beliefList.size();
beliefGridTimer.stop(); beliefGridTimer.stop();
@ -448,24 +450,28 @@ namespace storm {
std::map<uint64_t, std::vector<std::map<uint32_t, ValueType>>> observationProbabilities; std::map<uint64_t, std::vector<std::map<uint32_t, ValueType>>> observationProbabilities;
// current ID -> action -> next ID // current ID -> action -> next ID
std::map<uint64_t, std::vector<std::map<uint32_t, uint64_t>>> nextBelieves; std::map<uint64_t, std::vector<std::map<uint32_t, uint64_t>>> nextBelieves;
// current ID -> action -> reward
std::map<uint64_t, std::vector<ValueType>> beliefActionRewards;
storm::utility::Stopwatch nextBeliefGeneration(true); storm::utility::Stopwatch nextBeliefGeneration(true);
for (size_t i = 0; i < beliefGrid.size(); ++i) { for (size_t i = 0; i < beliefGrid.size(); ++i) {
auto currentBelief = beliefGrid[i]; auto currentBelief = beliefGrid[i];
bool isTarget = beliefIsTarget[currentBelief.id]; bool isTarget = beliefIsTarget[currentBelief.id];
if (isTarget) { if (isTarget) {
result.emplace(std::make_pair(currentBelief.id, storm::utility::one<ValueType>()));
result_backup.emplace(std::make_pair(currentBelief.id, storm::utility::one<ValueType>()));
result.emplace(std::make_pair(currentBelief.id, computeRewards ? storm::utility::zero<ValueType>() : storm::utility::one<ValueType>()));
result_backup.emplace(std::make_pair(currentBelief.id, computeRewards ? storm::utility::zero<ValueType>() : storm::utility::one<ValueType>()));
} else { } else {
result.emplace(std::make_pair(currentBelief.id, storm::utility::zero<ValueType>())); result.emplace(std::make_pair(currentBelief.id, storm::utility::zero<ValueType>()));
result_backup.emplace(std::make_pair(currentBelief.id, storm::utility::zero<ValueType>())); result_backup.emplace(std::make_pair(currentBelief.id, storm::utility::zero<ValueType>()));
//TODO put this in extra function //TODO put this in extra function
uint64_t numChoices = pomdp.getNumberOfChoices(
pomdp.getStatesWithObservation(currentBelief.observation).front());
// As we need to grab some parameters which are the same for all states with the same observation, we simply select some state as the representative
uint64_t representativeState = pomdp.getStatesWithObservation(currentBelief.observation).front();
uint64_t numChoices = pomdp.getNumberOfChoices(representativeState);
std::vector<std::map<uint32_t, ValueType>> observationProbabilitiesInAction(numChoices); std::vector<std::map<uint32_t, ValueType>> observationProbabilitiesInAction(numChoices);
std::vector<std::map<uint32_t, uint64_t>> nextBelievesInAction(numChoices); std::vector<std::map<uint32_t, uint64_t>> nextBelievesInAction(numChoices);
std::vector<ValueType> actionRewardsInState(numChoices);
for (uint64_t action = 0; action < numChoices; ++action) { for (uint64_t action = 0; action < numChoices; ++action) {
std::map<uint32_t, ValueType> actionObservationProbabilities = computeObservationProbabilitiesAfterAction(pomdp, currentBelief, action); std::map<uint32_t, ValueType> actionObservationProbabilities = computeObservationProbabilitiesAfterAction(pomdp, currentBelief, action);
std::map<uint32_t, uint64_t> actionObservationBelieves; std::map<uint32_t, uint64_t> actionObservationBelieves;
@ -473,23 +479,24 @@ namespace storm {
uint32_t observation = iter->first; uint32_t observation = iter->first;
// THIS CALL IS SLOW // THIS CALL IS SLOW
// TODO speed this up // TODO speed this up
actionObservationBelieves[observation] = getBeliefAfterActionAndObservation(pomdp,
beliefList,
beliefIsTarget,
targetObservations,
currentBelief,
action,
observation,
nextId);
actionObservationBelieves[observation] = getBeliefAfterActionAndObservation(pomdp, beliefList, beliefIsTarget, targetObservations, currentBelief,
action, observation, nextId);
nextId = beliefList.size(); nextId = beliefList.size();
} }
observationProbabilitiesInAction[action] = actionObservationProbabilities; observationProbabilitiesInAction[action] = actionObservationProbabilities;
nextBelievesInAction[action] = actionObservationBelieves; nextBelievesInAction[action] = actionObservationBelieves;
if (computeRewards) {
actionRewardsInState[action] = getRewardAfterAction(pomdp, pomdp.getChoiceIndex(storm::storage::StateActionPair(representativeState, action)),
currentBelief);
}
} }
observationProbabilities.emplace(
std::make_pair(currentBelief.id, observationProbabilitiesInAction));
observationProbabilities.emplace(std::make_pair(currentBelief.id, observationProbabilitiesInAction));
nextBelieves.emplace(std::make_pair(currentBelief.id, nextBelievesInAction)); nextBelieves.emplace(std::make_pair(currentBelief.id, nextBelievesInAction));
if (computeRewards) {
beliefActionRewards.emplace(std::make_pair(currentBelief.id, actionRewardsInState));
}
} }
} }
nextBeliefGeneration.stop(); nextBeliefGeneration.stop();
@ -508,16 +515,14 @@ namespace storm {
bool isTarget = beliefIsTarget[currentBelief.id]; bool isTarget = beliefIsTarget[currentBelief.id];
if (!isTarget) { if (!isTarget) {
// we can take any state with the observation as they have the same number of choices // 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 // 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; std::vector<uint64_t> chosenActionIndices;
ValueType currentValue; ValueType currentValue;
for (uint64_t action = 0; action < numChoices; ++action) { for (uint64_t action = 0; action < numChoices; ++action) {
currentValue = storm::utility::zero<ValueType>();
currentValue = computeRewards ? beliefActionRewards[currentBelief.id][action] : storm::utility::zero<ValueType>();
for (auto iter = observationProbabilities[currentBelief.id][action].begin(); for (auto iter = observationProbabilities[currentBelief.id][action].begin();
iter != observationProbabilities[currentBelief.id][action].end(); ++iter) { iter != observationProbabilities[currentBelief.id][action].end(); ++iter) {
uint32_t observation = iter->first; uint32_t observation = iter->first;
@ -530,8 +535,8 @@ namespace storm {
subSimplex = subSimplexCache[nextBelief.id]; subSimplex = subSimplexCache[nextBelief.id];
lambdas = lambdaCache[nextBelief.id]; lambdas = lambdaCache[nextBelief.id];
} else { } else {
std::pair<std::vector<std::vector<ValueType>>, std::vector<ValueType>> temp = computeSubSimplexAndLambdas(
nextBelief.probabilities, gridResolution);
std::pair<std::vector<std::vector<ValueType>>, std::vector<ValueType>> temp = computeSubSimplexAndLambdas(nextBelief.probabilities,
gridResolution);
subSimplex = temp.first; subSimplex = temp.first;
lambdas = temp.second; lambdas = temp.second;
subSimplexCache[nextBelief.id] = subSimplex; subSimplexCache[nextBelief.id] = subSimplex;
@ -540,16 +545,15 @@ namespace storm {
auto sum = storm::utility::zero<ValueType>(); auto sum = storm::utility::zero<ValueType>();
for (size_t j = 0; j < lambdas.size(); ++j) { for (size_t j = 0; j < lambdas.size(); ++j) {
if (!cc.isEqual(lambdas[j], storm::utility::zero<ValueType>())) { 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]));
} }
} }
currentValue += iter->second * sum; currentValue += iter->second * sum;
} }
// Update the selected actions // Update the selected actions
if ((min && cc.isLess(storm::utility::zero<ValueType>(), chosenValue - currentValue)) || 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)) { cc.isEqual(storm::utility::zero<ValueType>(), chosenValue - currentValue)) {
chosenValue = currentValue; chosenValue = currentValue;
if (!(useMdp && cc.isEqual(storm::utility::zero<ValueType>(), chosenValue - currentValue))) { if (!(useMdp && cc.isEqual(storm::utility::zero<ValueType>(), chosenValue - currentValue))) {
@ -561,7 +565,7 @@ namespace storm {
result[currentBelief.id] = chosenValue; result[currentBelief.id] = chosenValue;
chosenActions[currentBelief.id] = chosenActionIndices; chosenActions[currentBelief.id] = chosenActionIndices;
// Check if the iteration brought an improvement // Check if the iteration brought an improvement
if (cc.isLess(storm::utility::zero<ValueType>(), result[currentBelief.id] - result_backup[currentBelief.id])) {
if (!cc.isEqual(storm::utility::zero<ValueType>(), result_backup[currentBelief.id] - result[currentBelief.id])) {
improvement = true; improvement = true;
} }
} }
@ -580,17 +584,14 @@ namespace storm {
beliefGrid.push_back(initialBelief); beliefGrid.push_back(initialBelief);
beliefIsTarget.push_back(targetObservations.find(initialBelief.observation) != targetObservations.end()); beliefIsTarget.push_back(targetObservations.find(initialBelief.observation) != targetObservations.end());
std::pair<std::vector<std::vector<ValueType>>, std::vector<ValueType>> temp = computeSubSimplexAndLambdas(
initialBelief.probabilities, gridResolution);
std::pair<std::vector<std::vector<ValueType>>, std::vector<ValueType>> temp = computeSubSimplexAndLambdas(initialBelief.probabilities, gridResolution);
std::vector<std::vector<ValueType>> initSubSimplex = temp.first; std::vector<std::vector<ValueType>> initSubSimplex = temp.first;
std::vector<ValueType> initLambdas = temp.second; std::vector<ValueType> initLambdas = temp.second;
auto overApprox = storm::utility::zero<ValueType>(); auto overApprox = storm::utility::zero<ValueType>();
for (size_t j = 0; j < initLambdas.size(); ++j) { for (size_t j = 0; j < initLambdas.size(); ++j) {
if (initLambdas[j] != storm::utility::zero<ValueType>()) { 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(); overApproxTimer.stop();
@ -598,9 +599,9 @@ namespace storm {
// Now onto the under-approximation // Now onto the under-approximation
storm::utility::Stopwatch underApproxTimer(true); storm::utility::Stopwatch underApproxTimer(true);
ValueType underApprox = useMdp ? computeUnderapproximationWithMDP(pomdp, beliefList, beliefIsTarget, targetObservations, observationProbabilities, nextBelieves, ValueType underApprox = useMdp ? computeUnderapproximationWithMDP(pomdp, beliefList, beliefIsTarget, targetObservations, observationProbabilities, nextBelieves,
result, chosenActions, gridResolution, initialBelief.id, min, false) :
computeUnderapproximationWithDTMC(pomdp, beliefList, beliefIsTarget, targetObservations, observationProbabilities, nextBelieves, result,
chosenActions, gridResolution, initialBelief.id, min, false);
result, chosenActions, gridResolution, initialBelief.id, min, computeRewards) :
computeUnderapproximationWithDTMC(pomdp, beliefList, beliefIsTarget, targetObservations, observationProbabilities, nextBelieves,
result, chosenActions, gridResolution, initialBelief.id, min, computeRewards);
underApproxTimer.stop(); underApproxTimer.stop();
STORM_PRINT("Time Belief Grid Generation: " << beliefGridTimer << std::endl STORM_PRINT("Time Belief Grid Generation: " << beliefGridTimer << std::endl
@ -608,231 +609,24 @@ namespace storm {
<< std::endl << std::endl
<< "Time Underapproximation: " << underApproxTimer << "Time Underapproximation: " << underApproxTimer
<< std::endl); << std::endl);
STORM_PRINT("Over-Approximation Result: " << overApprox << 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});
return std::make_unique<POMDPCheckResult<ValueType>>(POMDPCheckResult<ValueType>{overApprox, underApprox});
}
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) {
return computeReachability(pomdp, targetObservations, min, gridResolution, false);
} }
//TODO This function reuses a lot of code from the probability computation, refactor to minimize code duplication!
template<typename ValueType, typename RewardModelType> template<typename ValueType, typename RewardModelType>
std::unique_ptr<POMDPCheckResult<ValueType>> std::unique_ptr<POMDPCheckResult<ValueType>>
ApproximatePOMDPModelchecker<ValueType, RewardModelType>::computeReachabilityReward(storm::models::sparse::Pomdp<ValueType, RewardModelType> const &pomdp, ApproximatePOMDPModelchecker<ValueType, RewardModelType>::computeReachabilityReward(storm::models::sparse::Pomdp<ValueType, RewardModelType> const &pomdp,
std::set<uint32_t> targetObservations, bool min, uint64_t gridResolution) { std::set<uint32_t> targetObservations, bool min, uint64_t gridResolution) {
storm::utility::Stopwatch beliefGridTimer(true);
bool finished = false;
uint64_t iteration = 0;
RewardModelType pomdpRewardModel = pomdp.getUniqueRewardModel();
std::vector<storm::pomdp::Belief<ValueType>> beliefList;
std::vector<bool> beliefIsTarget;
uint64_t nextId = 0;
// 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());
std::vector<storm::pomdp::Belief<ValueType>> beliefGrid;
constructBeliefGrid(pomdp, targetObservations, gridResolution, beliefList, beliefGrid, beliefIsTarget,
nextId);
nextId = beliefList.size();
beliefGridTimer.stop();
storm::utility::Stopwatch overApproxTimer(true);
// Belief ID -> Value
std::map<uint64_t, ValueType> result;
std::map<uint64_t, ValueType> result_backup;
// Belief ID -> ActionIndex
std::map<uint64_t, std::vector<uint64_t>> chosenActions;
// 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;
// current ID -> action -> reward
std::map<uint64_t, std::vector<ValueType>> beliefActionRewards;
storm::utility::Stopwatch nextBeliefGeneration(true);
for (size_t i = 0; i < beliefGrid.size(); ++i) {
auto currentBelief = beliefGrid[i];
bool isTarget = beliefIsTarget[currentBelief.id];
result.emplace(std::make_pair(currentBelief.id, storm::utility::zero<ValueType>()));
result_backup.emplace(std::make_pair(currentBelief.id, storm::utility::zero<ValueType>()));
if (!isTarget) {
//TODO put this in extra function
// As we need to grab some parameters which are the same for all states with the same observation, we simply select some state as the representative
uint64_t representativeState = pomdp.getStatesWithObservation(currentBelief.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);
for (uint64_t action = 0; action < numChoices; ++action) {
std::map<uint32_t, ValueType> actionObservationProbabilities = computeObservationProbabilitiesAfterAction(
pomdp, currentBelief, action);
std::map<uint32_t, uint64_t> actionObservationBelieves;
for (auto iter = actionObservationProbabilities.begin();
iter != actionObservationProbabilities.end(); ++iter) {
uint32_t observation = iter->first;
// THIS CALL IS SLOW
// TODO speed this up
actionObservationBelieves[observation] = getBeliefAfterActionAndObservation(pomdp,
beliefList,
beliefIsTarget,
targetObservations,
currentBelief,
action,
observation,
nextId);
nextId = beliefList.size();
}
observationProbabilitiesInAction[action] = actionObservationProbabilities;
nextBelievesInAction[action] = actionObservationBelieves;
actionRewardsInState[action] = getRewardAfterAction(pomdp, pomdp.getChoiceIndex(storm::storage::StateActionPair(representativeState, action)),
currentBelief);
}
observationProbabilities.emplace(
std::make_pair(currentBelief.id, observationProbabilitiesInAction));
nextBelieves.emplace(std::make_pair(currentBelief.id, nextBelievesInAction));
beliefActionRewards.emplace(std::make_pair(currentBelief.id, actionRewardsInState));
}
}
nextBeliefGeneration.stop();
//Use chaching 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;
STORM_PRINT("Time generation of next believes: " << nextBeliefGeneration << std::endl)
// 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 = beliefActionRewards[currentBelief.id][action];
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 {
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_backup[currentBelief.id] - result[currentBelief.id]) ||
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);
beliefGrid.push_back(initialBelief);
beliefIsTarget.push_back(
targetObservations.find(initialBelief.observation) != targetObservations.end());
std::pair<std::vector<std::vector<ValueType>>, std::vector<ValueType>> temp = computeSubSimplexAndLambdas(
initialBelief.probabilities, gridResolution);
std::vector<std::vector<ValueType>> initSubSimplex = temp.first;
std::vector<ValueType> initLambdas = temp.second;
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])];
}
}
overApproxTimer.stop();
// Now onto the under-approximation
storm::utility::Stopwatch underApproxTimer(true);
ValueType underApprox = useMdp ? computeUnderapproximationWithMDP(pomdp, beliefList, beliefIsTarget, targetObservations, observationProbabilities, nextBelieves,
result, chosenActions, gridResolution, initialBelief.id, min, true) :
computeUnderapproximationWithDTMC(pomdp, beliefList, beliefIsTarget, targetObservations, observationProbabilities, nextBelieves,
result, chosenActions, gridResolution, initialBelief.id, min, true);
underApproxTimer.stop();
STORM_PRINT("Time Belief Grid Generation: " << beliefGridTimer << std::endl
<< "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);
return std::make_unique<POMDPCheckResult<ValueType>>(
POMDPCheckResult<ValueType>{overApprox, underApprox});
return computeReachability(pomdp, targetObservations, min, gridResolution, true);
} }
template<typename ValueType, typename RewardModelType> template<typename ValueType, typename RewardModelType>

14
src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.h
View File

@ -59,6 +59,20 @@ namespace storm {
std::set<uint32_t> targetObservations, bool min, std::set<uint32_t> targetObservations, bool min,
uint64_t gridResolution, bool computeRewards); uint64_t gridResolution, bool computeRewards);
/**
*
* @param pomdp
* @param targetObservations
* @param min
* @param gridResolution
* @param computeRewards
* @return
*/
std::unique_ptr<POMDPCheckResult<ValueType>>
computeReachability(storm::models::sparse::Pomdp<ValueType, RewardModelType> const &pomdp,
std::set<uint32_t> targetObservations, bool min,
uint64_t gridResolution, bool computeRewards);
/** /**
* TODO * TODO
* @param pomdp * @param pomdp

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