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Removal of unused extractActions function

tempestpy_adaptions
Alexander Bork 5 years ago
parent
e1bd87b91a
commit
c74a6ea201
2 changed files with 8 additions and 143 deletions
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  1. 101
      src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.cpp
  2. 46
      src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.h

101
src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.cpp
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@ -295,7 +295,7 @@ namespace storm {
//beliefsToBeExpanded.push_back(initialBelief.id); 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 //beliefsToBeExpanded.push_back(initialBelief.id); 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 // Expand the beliefs to generate the grid on-the-fly
if(explorationThreshold > 0){
if (explorationThreshold > 0) {
STORM_PRINT("Exploration threshold: " << explorationThreshold << std::endl) STORM_PRINT("Exploration threshold: " << explorationThreshold << std::endl)
} }
while (!beliefsToBeExpanded.empty()) { while (!beliefsToBeExpanded.empty()) {
@ -340,7 +340,7 @@ namespace storm {
observationResolutionVector[beliefList[idNextBelief].observation], pomdp.getNumberOfStates()); observationResolutionVector[beliefList[idNextBelief].observation], pomdp.getNumberOfStates());
subSimplex = temp.first; subSimplex = temp.first;
lambdas = temp.second; lambdas = temp.second;
if(cacheSubsimplices){
if (cacheSubsimplices) {
subSimplexCache[idNextBelief] = subSimplex; subSimplexCache[idNextBelief] = subSimplex;
lambdaCache[idNextBelief] = lambdas; lambdaCache[idNextBelief] = lambdas;
} }
@ -525,7 +525,7 @@ namespace storm {
auto temp = computeSubSimplexAndLambdas(nextBelief.probabilities, gridResolution, pomdp.getNumberOfStates()); auto temp = computeSubSimplexAndLambdas(nextBelief.probabilities, gridResolution, pomdp.getNumberOfStates());
subSimplex = temp.first; subSimplex = temp.first;
lambdas = temp.second; lambdas = temp.second;
if(cacheSubsimplices) {
if (cacheSubsimplices) {
subSimplexCache[nextBelief.id] = subSimplex; subSimplexCache[nextBelief.id] = subSimplex;
lambdaCache[nextBelief.id] = lambdas; lambdaCache[nextBelief.id] = lambdas;
} }
@ -573,7 +573,7 @@ namespace storm {
std::vector<ValueType> initialLambda; std::vector<ValueType> initialLambda;
std::vector<std::map<uint64_t, ValueType>> initialSubsimplex; std::vector<std::map<uint64_t, ValueType>> initialSubsimplex;
if(cacheSubsimplices){
if (cacheSubsimplices) {
initialLambda = lambdaCache[0]; initialLambda = lambdaCache[0];
initialSubsimplex = subSimplexCache[0]; initialSubsimplex = subSimplexCache[0];
} else { } else {
@ -647,7 +647,7 @@ namespace storm {
std::map<uint64_t, std::vector<ValueType>> lambdaCache; std::map<uint64_t, std::vector<ValueType>> lambdaCache;
auto temp = computeSubSimplexAndLambdas(initialBelief.probabilities, gridResolution, pomdp.getNumberOfStates()); auto temp = computeSubSimplexAndLambdas(initialBelief.probabilities, gridResolution, pomdp.getNumberOfStates());
if(cacheSubsimplices) {
if (cacheSubsimplices) {
subSimplexCache[0] = temp.first; subSimplexCache[0] = temp.first;
lambdaCache[0] = temp.second; lambdaCache[0] = temp.second;
} }
@ -884,96 +884,6 @@ namespace storm {
return smb.build(); return smb.build();
} }
template<typename ValueType, typename RewardModelType>
std::vector<uint64_t> ApproximatePOMDPModelchecker<ValueType, RewardModelType>::extractBestActions(
storm::models::sparse::Pomdp<ValueType, RewardModelType> const &pomdp,
std::vector<storm::pomdp::Belief<ValueType>> &beliefList,
std::vector<bool> &beliefIsTarget,
std::set<uint32_t> const &targetObservations,
std::map<uint64_t, std::vector<std::map<uint32_t, ValueType>>> &observationProbabilities,
std::map<uint64_t, std::vector<std::map<uint32_t, uint64_t>>> &nextBelieves,
std::map<uint64_t, ValueType> &result,
uint64_t gridResolution, uint64_t currentBeliefId, uint64_t nextId, bool min) {
storm::pomdp::Belief<ValueType> currentBelief = beliefList[currentBeliefId];
//TODO put this in extra function
std::vector<std::map<uint32_t, ValueType>> observationProbabilitiesInAction;
std::vector<std::map<uint32_t, uint64_t>> nextBelievesInAction;
uint64_t numChoices = pomdp.getNumberOfChoices(
pomdp.getStatesWithObservation(currentBelief.observation).front());
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;
actionObservationBelieves[observation] = getBeliefAfterActionAndObservation(pomdp, beliefList, beliefIsTarget, targetObservations, currentBelief,
action, observation, nextId);
nextId = beliefList.size();
}
observationProbabilitiesInAction.push_back(actionObservationProbabilities);
nextBelievesInAction.push_back(actionObservationBelieves);
}
observationProbabilities.emplace(std::make_pair(currentBeliefId, observationProbabilitiesInAction));
nextBelieves.emplace(std::make_pair(currentBeliefId, nextBelievesInAction));
// choose the action which results in the value computed by the over-approximation
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>(); // simply change this for rewards?
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
auto temp = computeSubSimplexAndLambdas(nextBelief.probabilities, gridResolution, pomdp.getNumberOfStates());
std::vector<std::map<uint64_t, ValueType>> subSimplex = temp.first;
std::vector<ValueType> lambdas = temp.second;
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.at(getBeliefIdInVector(beliefList, 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 (!cc.isEqual(storm::utility::zero<ValueType>(), chosenValue - currentValue)) {
chosenActionIndices.clear();
}
chosenActionIndices.push_back(action);
}
}
return chosenActionIndices;
}
template<typename ValueType, typename RewardModelType>
std::vector<uint64_t> ApproximatePOMDPModelchecker<ValueType, RewardModelType>::extractBestAction(
storm::models::sparse::Pomdp<ValueType, RewardModelType> const &pomdp,
std::vector<storm::pomdp::Belief<ValueType>> &beliefList,
std::vector<bool> &beliefIsTarget,
std::set<uint32_t> const &targetObservations,
std::map<uint64_t, std::vector<std::map<uint32_t, ValueType>>> &observationProbabilities,
std::map<uint64_t, std::vector<std::map<uint32_t, uint64_t>>> &nextBelieves,
std::map<uint64_t, ValueType> &result,
uint64_t gridResolution, uint64_t currentBeliefId, uint64_t nextId, bool min) {
return std::vector<uint64_t>{
extractBestActions(pomdp, beliefList, beliefIsTarget, targetObservations, observationProbabilities, nextBelieves, result, gridResolution, currentBeliefId,
nextId, min).front()};
}
template<typename ValueType, typename RewardModelType> template<typename ValueType, typename RewardModelType>
uint64_t ApproximatePOMDPModelchecker<ValueType, RewardModelType>::getBeliefIdInVector( uint64_t ApproximatePOMDPModelchecker<ValueType, RewardModelType>::getBeliefIdInVector(
std::vector<storm::pomdp::Belief<ValueType>> const &grid, uint32_t observation, std::vector<storm::pomdp::Belief<ValueType>> const &grid, uint32_t observation,
@ -1265,6 +1175,7 @@ namespace storm {
class ApproximatePOMDPModelchecker<double>; class ApproximatePOMDPModelchecker<double>;
#ifdef STORM_HAVE_CARL #ifdef STORM_HAVE_CARL
template template
class ApproximatePOMDPModelchecker<storm::RationalNumber>; class ApproximatePOMDPModelchecker<storm::RationalNumber>;

46
src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.h
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@ -179,52 +179,6 @@ namespace storm {
std::set<uint32_t> const &targetObservations, bool min, std::set<uint32_t> const &targetObservations, bool min,
uint64_t gridResolution, bool computeRewards); uint64_t gridResolution, bool computeRewards);
/**
* TODO
* @param pomdp
* @param beliefList
* @param observationProbabilities
* @param nextBelieves
* @param result
* @param gridResolution
* @param currentBeliefId
* @param nextId
* @param min
* @return
*/
std::vector<uint64_t> extractBestActions(storm::models::sparse::Pomdp<ValueType, RewardModelType> const &pomdp,
std::vector<storm::pomdp::Belief<ValueType>> &beliefList,
std::vector<bool> &beliefIsTarget,
std::set<uint32_t> const &target_observations,
std::map<uint64_t, std::vector<std::map<uint32_t, ValueType>>> &observationProbabilities,
std::map<uint64_t, std::vector<std::map<uint32_t, uint64_t>>> &nextBelieves,
std::map<uint64_t, ValueType> &result,
uint64_t gridResolution, uint64_t currentBeliefId, uint64_t nextId,
bool min);
/**
* TODO
* @param pomdp
* @param beliefList
* @param observationProbabilities
* @param nextBelieves
* @param result
* @param gridResolution
* @param currentBeliefId
* @param nextId
* @param min
* @return
*/
std::vector<uint64_t> extractBestAction(storm::models::sparse::Pomdp<ValueType, RewardModelType> const &pomdp,
std::vector<storm::pomdp::Belief<ValueType>> &beliefList,
std::vector<bool> &beliefIsTarget,
std::set<uint32_t> const &target_observations,
std::map<uint64_t, std::vector<std::map<uint32_t, ValueType>>> &observationProbabilities,
std::map<uint64_t, std::vector<std::map<uint32_t, uint64_t>>> &nextBelieves,
std::map<uint64_t, ValueType> &result,
uint64_t gridResolution, uint64_t currentBeliefId, uint64_t nextId,
bool min);
/** /**
* Helper to compute an underapproximation of the reachability property. * Helper to compute an underapproximation of the reachability property.
* The implemented method unrolls the belief support of the given POMDP up to a given number of belief states. * The implemented method unrolls the belief support of the given POMDP up to a given number of belief states.

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