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Changed data structure for belief distributions from vector to map to exploit sparsity

tempestpy_adaptions
Alexander Bork 5 years ago
parent
e02ea57a58
commit
91232a2b11
3 changed files with 101 additions and 103 deletions
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  1. 192
      src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.cpp
  2. 8
      src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.h
  3. 2
      src/storm-pomdp/storage/Belief.h

192
src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.cpp
View File

@ -113,7 +113,7 @@ namespace storm {
} else {
//otherwise, we approximate a value TODO this is critical, we have to think about it
auto overApproxValue = storm::utility::zero<ValueType>();
auto temp = computeSubSimplexAndLambdas(currentBelief.probabilities, observationResolutionVector[currentBelief.observation]);
auto temp = computeSubSimplexAndLambdas(currentBelief.probabilities, observationResolutionVector[currentBelief.observation], pomdp.getNumberOfStates());
auto subSimplex = temp.first;
auto lambdas = temp.second;
for (size_t j = 0; j < lambdas.size(); ++j) {
@ -190,16 +190,12 @@ namespace storm {
std::vector<bool> beliefIsTarget;
std::vector<storm::pomdp::Belief<ValueType>> beliefGrid;
//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<std::map<uint64_t, ValueType>>> subSimplexCache;
std::map<uint64_t, std::vector<ValueType>> lambdaCache;
bsmap_type beliefStateMap;
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;
// current ID -> action -> reward
std::map<uint64_t, std::vector<ValueType>> beliefActionRewards;
uint64_t nextId = 0;
@ -227,10 +223,9 @@ namespace storm {
std::map<uint64_t, ValueType> weightedSumUnderMap;
// for the initial belief, add the triangulated initial states
std::pair<std::vector<std::vector<ValueType>>, std::vector<ValueType>> initTemp = computeSubSimplexAndLambdas(initialBelief.probabilities,
observationResolutionVector[initialBelief.observation]);
std::vector<std::vector<ValueType>> initSubSimplex = initTemp.first;
std::vector<ValueType> initLambdas = initTemp.second;
auto initTemp = computeSubSimplexAndLambdas(initialBelief.probabilities, observationResolutionVector[initialBelief.observation], pomdp.getNumberOfStates());
auto initSubSimplex = initTemp.first;
auto initLambdas = initTemp.second;
if (cacheSubsimplices) {
subSimplexCache[0] = initSubSimplex;
lambdaCache[0] = initLambdas;
@ -297,7 +292,6 @@ namespace storm {
initTransitionsInBelief.push_back(initTransitionInActionBelief);
mdpTransitions.push_back(initTransitionsInBelief);
}
//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
@ -305,7 +299,7 @@ namespace storm {
STORM_PRINT("Exploration threshold: " << explorationThreshold << std::endl)
}
while (!beliefsToBeExpanded.empty()) {
// TODO add direct generation of transition matrix
// TODO direct generation of transition matrix?
uint64_t currId = beliefsToBeExpanded.front();
beliefsToBeExpanded.pop_front();
bool isTarget = beliefIsTarget[currId];
@ -323,14 +317,11 @@ namespace storm {
} 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;
@ -339,16 +330,15 @@ namespace storm {
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<std::map<uint64_t, ValueType>> subSimplex;
std::vector<ValueType> lambdas;
if (cacheSubsimplices && subSimplexCache.count(idNextBelief) > 0) {
subSimplex = subSimplexCache[idNextBelief];
lambdas = lambdaCache[idNextBelief];
} else {
std::pair<std::vector<std::vector<ValueType>>, std::vector<ValueType>> temp = computeSubSimplexAndLambdas(
beliefList[idNextBelief].probabilities, observationResolutionVector[beliefList[idNextBelief].observation]);
auto temp = computeSubSimplexAndLambdas(beliefList[idNextBelief].probabilities,
observationResolutionVector[beliefList[idNextBelief].observation], pomdp.getNumberOfStates());
subSimplex = temp.first;
lambdas = temp.second;
if(cacheSubsimplices){
@ -397,8 +387,6 @@ namespace storm {
}
}
}
observationProbabilitiesInAction[action] = actionObservationProbabilities;
nextBelievesInAction[action] = actionObservationBelieves;
if (computeRewards) {
actionRewardsInState[action] = getRewardAfterAction(pomdp, pomdp.getChoiceIndex(storm::storage::StateActionPair(representativeState, action)),
beliefList[currId]);
@ -407,8 +395,6 @@ namespace storm {
transitionsInBelief.push_back(transitionInActionBelief);
}
}
observationProbabilities.emplace(std::make_pair(currId, observationProbabilitiesInAction));
nextBelieves.emplace(std::make_pair(currId, nextBelievesInAction));
if (computeRewards) {
beliefActionRewards.emplace(std::make_pair(currId, actionRewardsInState));
}
@ -499,7 +485,7 @@ namespace storm {
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, std::vector<ValueType>> &beliefActionRewards,
std::map<uint64_t, std::vector<std::vector<ValueType>>> &subSimplexCache,
std::map<uint64_t, std::vector<std::map<uint64_t, ValueType>>> &subSimplexCache,
std::map<uint64_t, std::vector<ValueType>> &lambdaCache,
std::map<uint64_t, ValueType> &result,
std::map<uint64_t, std::vector<uint64_t>> &chosenActions,
@ -531,14 +517,13 @@ namespace storm {
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<std::map<uint64_t, ValueType>> subSimplex;
std::vector<ValueType> lambdas;
if (cacheSubsimplices && 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);
auto temp = computeSubSimplexAndLambdas(nextBelief.probabilities, gridResolution, pomdp.getNumberOfStates());
subSimplex = temp.first;
lambdas = temp.second;
if(cacheSubsimplices) {
@ -588,13 +573,13 @@ namespace storm {
STORM_PRINT("Overapproximation took " << iteration << " iterations" << std::endl);
std::vector<ValueType> initialLambda;
std::vector<std::vector<ValueType>> initialSubsimplex;
std::vector<std::map<uint64_t, ValueType>> initialSubsimplex;
if(cacheSubsimplices){
initialLambda = lambdaCache[0];
initialSubsimplex = subSimplexCache[0];
} else {
auto temp = computeSubSimplexAndLambdas(beliefList[0].probabilities, gridResolution);
initialSubsimplex= temp.first;
auto temp = computeSubSimplexAndLambdas(beliefList[0].probabilities, gridResolution, pomdp.getNumberOfStates());
initialSubsimplex = temp.first;
initialLambda = temp.second;
}
@ -659,10 +644,10 @@ namespace storm {
// current ID -> action -> reward
std::map<uint64_t, std::vector<ValueType>> beliefActionRewards;
//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<std::map<uint64_t, ValueType>>> subSimplexCache;
std::map<uint64_t, std::vector<ValueType>> lambdaCache;
std::pair<std::vector<std::vector<ValueType>>, std::vector<ValueType>> temp = computeSubSimplexAndLambdas(initialBelief.probabilities, gridResolution);
auto temp = computeSubSimplexAndLambdas(initialBelief.probabilities, gridResolution, pomdp.getNumberOfStates());
if(cacheSubsimplices) {
subSimplexCache[0] = temp.first;
lambdaCache[0] = temp.second;
@ -946,8 +931,8 @@ namespace storm {
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);
std::vector<std::vector<ValueType>> subSimplex = temp.first;
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>();
@ -993,13 +978,17 @@ namespace storm {
template<typename ValueType, typename RewardModelType>
uint64_t ApproximatePOMDPModelchecker<ValueType, RewardModelType>::getBeliefIdInVector(
std::vector<storm::pomdp::Belief<ValueType>> const &grid, uint32_t observation,
std::vector<ValueType> &probabilities) {
std::map<uint64_t, ValueType> &probabilities) {
// TODO This one is quite slow
for (auto const &belief : grid) {
if (belief.observation == observation) {
bool same = true;
for (size_t i = 0; i < belief.probabilities.size(); ++i) {
if (!cc.isEqual(belief.probabilities[i], probabilities[i])) {
for (auto const &probEntry : belief.probabilities) {
if (probabilities.find(probEntry.first) == probabilities.end()) {
same = false;
break;
}
if (!cc.isEqual(probEntry.second, probabilities[probEntry.first])) {
same = false;
break;
}
@ -1009,7 +998,6 @@ namespace storm {
}
}
}
return -1;
}
@ -1020,12 +1008,13 @@ namespace storm {
"POMDP contains more than one initial state");
STORM_LOG_ASSERT(pomdp.getInitialStates().getNumberOfSetBits() == 1,
"POMDP does not contain an initial state");
std::vector<ValueType> distribution(pomdp.getNumberOfStates(), storm::utility::zero<ValueType>());
std::map<uint64_t, ValueType> distribution;
uint32_t observation = 0;
for (uint64_t state = 0; state < pomdp.getNumberOfStates(); ++state) {
if (pomdp.getInitialStates()[state] == 1) {
distribution[state] = storm::utility::one<ValueType>();
observation = pomdp.getObservation(state);
break;
}
}
return storm::pomdp::Belief<ValueType>{id, observation, distribution};
@ -1048,8 +1037,7 @@ namespace storm {
// TODO this can probably be condensed
if (statesWithObservation.size() == 1) {
// If there is only one state with the observation, we can directly add the corresponding belief
std::vector<ValueType> distribution(pomdp.getNumberOfStates(),
storm::utility::zero<ValueType>());
std::map<uint64_t, ValueType> distribution;
distribution[statesWithObservation.front()] = storm::utility::one<ValueType>();
storm::pomdp::Belief<ValueType> belief = {newId, observation, distribution};
STORM_LOG_TRACE(
@ -1068,17 +1056,19 @@ namespace storm {
uint64_t index = 0;
while (!done) {
std::vector<ValueType> distribution(pomdp.getNumberOfStates(),
storm::utility::zero<ValueType>());
std::map<uint64_t, ValueType> distribution;
for (size_t i = 0; i < statesWithObservation.size() - 1; ++i) {
distribution[statesWithObservation[i]] = (helper[i] - helper[i + 1]) /
storm::utility::convertNumber<ValueType>(
gridResolution);
if (helper[i] - helper[i + 1] > ValueType(0)) {
distribution[statesWithObservation[i]] = (helper[i] - helper[i + 1]) /
storm::utility::convertNumber<ValueType>(
gridResolution);
}
}
if (helper[statesWithObservation.size() - 1] > ValueType(0)) {
distribution[statesWithObservation.back()] =
helper[statesWithObservation.size() - 1] /
storm::utility::convertNumber<ValueType>(gridResolution);
}
distribution[statesWithObservation.back()] =
helper[statesWithObservation.size() - 1] /
storm::utility::convertNumber<ValueType>(gridResolution);
storm::pomdp::Belief<ValueType> belief = {newId, observation, distribution};
STORM_LOG_TRACE("Add Belief " << std::to_string(newId) << " [(" << std::to_string(observation) << ")," << distribution << "]");
beliefList.push_back(belief);
@ -1111,20 +1101,24 @@ namespace storm {
}
template<typename ValueType, typename RewardModelType>
std::pair<std::vector<std::vector<ValueType>>, std::vector<ValueType>>
std::pair<std::vector<std::map<uint64_t, ValueType>>, std::vector<ValueType>>
ApproximatePOMDPModelchecker<ValueType, RewardModelType>::computeSubSimplexAndLambdas(
std::vector<ValueType> &probabilities, uint64_t resolution) {
std::map<uint64_t, ValueType> &probabilities, uint64_t resolution, uint64_t nrStates) {
//TODO this can also be simplified using the sparse vector interpretation
// This is the Freudenthal Triangulation as described in Lovejoy (a whole lotta math)
// Variable names are based on the paper
uint64_t probSize = probabilities.size();
std::vector<ValueType> x(probSize);
std::vector<ValueType> v(probSize);
std::vector<ValueType> d(probSize);
std::vector<ValueType> x(nrStates);
std::vector<ValueType> v(nrStates);
std::vector<ValueType> d(nrStates);
auto convResolution = storm::utility::convertNumber<ValueType>(resolution);
for (size_t i = 0; i < probSize; ++i) {
for (size_t j = i; j < probSize; ++j) {
x[i] += convResolution * probabilities[j];
for (size_t i = 0; i < nrStates; ++i) {
for (auto const &probEntry : probabilities) {
if (probEntry.first >= i) {
x[i] += convResolution * probEntry.second;
}
}
v[i] = storm::utility::floor(x[i]);
d[i] = x[i] - v[i];
@ -1132,14 +1126,14 @@ namespace storm {
auto p = storm::utility::vector::getSortedIndices(d);
std::vector<std::vector<ValueType>> qs(probSize, std::vector<ValueType>(probSize));
for (size_t i = 0; i < probSize; ++i) {
std::vector<std::vector<ValueType>> qs(nrStates, std::vector<ValueType>(nrStates));
for (size_t i = 0; i < nrStates; ++i) {
if (i == 0) {
for (size_t j = 0; j < probSize; ++j) {
for (size_t j = 0; j < nrStates; ++j) {
qs[i][j] = v[j];
}
} else {
for (size_t j = 0; j < probSize; ++j) {
for (size_t j = 0; j < nrStates; ++j) {
if (j == p[i - 1]) {
qs[i][j] = qs[i - 1][j] + storm::utility::one<ValueType>();
} else {
@ -1148,18 +1142,22 @@ namespace storm {
}
}
}
std::vector<std::vector<ValueType>> subSimplex(probSize, std::vector<ValueType>(probSize));
for (size_t j = 0; j < probSize; ++j) {
for (size_t i = 0; i < probSize - 1; ++i) {
subSimplex[j][i] = (qs[j][i] - qs[j][i + 1]) / convResolution;
std::vector<std::map<uint64_t, ValueType>> subSimplex(nrStates);
for (size_t j = 0; j < nrStates; ++j) {
for (size_t i = 0; i < nrStates - 1; ++i) {
if (cc.isLess(storm::utility::zero<ValueType>(), qs[j][i] - qs[j][i + 1])) {
subSimplex[j][i] = (qs[j][i] - qs[j][i + 1]) / convResolution;
}
}
subSimplex[j][probSize - 1] = qs[j][probSize - 1] / convResolution;
if (cc.isLess(storm::utility::zero<ValueType>(), qs[j][nrStates - 1])) {
subSimplex[j][nrStates - 1] = qs[j][nrStates - 1] / convResolution;
}
}
std::vector<ValueType> lambdas(probSize, storm::utility::zero<ValueType>());
std::vector<ValueType> lambdas(nrStates, storm::utility::zero<ValueType>());
auto sum = storm::utility::zero<ValueType>();
for (size_t i = 1; i < probSize; ++i) {
for (size_t i = 1; i < nrStates; ++i) {
lambdas[i] = d[p[i - 1]] - d[p[i]];
sum += d[p[i - 1]] - d[p[i]];
}
@ -1177,17 +1175,16 @@ namespace storm {
uint64_t actionIndex) {
std::map<uint32_t, ValueType> res;
// the id is not important here as we immediately discard the belief (very hacky, I don't like it either)
std::vector<ValueType> postProbabilities = getBeliefAfterAction(pomdp, belief, actionIndex, 0).probabilities;
for (uint64_t state = 0; state < pomdp.getNumberOfStates(); ++state) {
uint32_t observation = pomdp.getObservation(state);
if (postProbabilities[state] != storm::utility::zero<ValueType>()) {
if (res.count(observation) == 0) {
res[observation] = postProbabilities[state];
} else {
res[observation] += postProbabilities[state];
}
std::map<uint64_t, ValueType> postProbabilities = getBeliefAfterAction(pomdp, belief, actionIndex, 0).probabilities;
for (auto const &probEntry : postProbabilities) {
uint32_t observation = pomdp.getObservation(probEntry.first);
if (res.count(observation) == 0) {
res[observation] = probEntry.second;
} else {
res[observation] += probEntry.second;
}
}
return res;
}
@ -1195,12 +1192,13 @@ namespace storm {
storm::pomdp::Belief<ValueType>
ApproximatePOMDPModelchecker<ValueType, RewardModelType>::getBeliefAfterAction(storm::models::sparse::Pomdp<ValueType, RewardModelType> const &pomdp,
storm::pomdp::Belief<ValueType> &belief, uint64_t actionIndex, uint64_t id) {
std::vector<ValueType> distributionAfter(pomdp.getNumberOfStates(), storm::utility::zero<ValueType>());
std::map<uint64_t, ValueType> distributionAfter;
uint32_t observation = 0;
for (uint64_t state = 0; state < pomdp.getNumberOfStates(); ++state) {
if (belief.probabilities[state] != storm::utility::zero<ValueType>()) {
auto row = pomdp.getTransitionMatrix().getRow(pomdp.getChoiceIndex(storm::storage::StateActionPair(state, actionIndex)));
for (auto const &entry : row) {
for (auto const &probEntry : belief.probabilities) {
uint64_t state = probEntry.first;
auto row = pomdp.getTransitionMatrix().getRow(pomdp.getChoiceIndex(storm::storage::StateActionPair(state, actionIndex)));
for (auto const &entry : row) {
if (entry.getValue() > 0) {
observation = pomdp.getObservation(entry.getColumn());
distributionAfter[entry.getColumn()] += belief.probabilities[state] * entry.getValue();
}
@ -1215,14 +1213,13 @@ namespace storm {
std::vector<bool> &beliefIsTarget, std::set<uint32_t> const &targetObservations, storm::pomdp::Belief<ValueType> &belief, uint64_t actionIndex,
uint32_t observation, uint64_t id) {
storm::utility::Stopwatch distrWatch(true);
std::vector<ValueType> distributionAfter(pomdp.getNumberOfStates()); //, storm::utility::zero<ValueType>());
for (uint64_t state = 0; state < pomdp.getNumberOfStates(); ++state) {
if (belief.probabilities[state] != storm::utility::zero<ValueType>()) {
auto row = pomdp.getTransitionMatrix().getRow(pomdp.getChoiceIndex(storm::storage::StateActionPair(state, actionIndex)));
for (auto const &entry : row) {
if (pomdp.getObservation(entry.getColumn()) == observation) {
distributionAfter[entry.getColumn()] += belief.probabilities[state] * entry.getValue();
}
std::map<uint64_t, ValueType> distributionAfter;
for (auto const &probEntry : belief.probabilities) {
uint64_t state = probEntry.first;
auto row = pomdp.getTransitionMatrix().getRow(pomdp.getChoiceIndex(storm::storage::StateActionPair(state, actionIndex)));
for (auto const &entry : row) {
if (pomdp.getObservation(entry.getColumn()) == observation) {
distributionAfter[entry.getColumn()] += belief.probabilities[state] * entry.getValue();
}
}
}
@ -1230,12 +1227,12 @@ namespace storm {
// We have to normalize the distribution
storm::utility::Stopwatch normalizationWatch(true);
auto sum = storm::utility::zero<ValueType>();
for (ValueType const &entry : distributionAfter) {
sum += entry;
for (auto const &entry : distributionAfter) {
sum += entry.second;
}
for (size_t i = 0; i < pomdp.getNumberOfStates(); ++i) {
distributionAfter[i] /= sum;
for (auto const &entry : distributionAfter) {
distributionAfter[entry.first] /= sum;
}
normalizationWatch.stop();
if (getBeliefIdInVector(beliefList, observation, distributionAfter) != uint64_t(-1)) {
@ -1259,7 +1256,8 @@ namespace storm {
uint64_t action, storm::pomdp::Belief<ValueType> &belief) {
auto result = storm::utility::zero<ValueType>();
for (size_t i = 0; i < belief.probabilities.size(); ++i) {
result += belief.probabilities[i] * pomdp.getUniqueRewardModel().getTotalStateActionReward(i, action, pomdp.getTransitionMatrix());
for (auto const &probEntry : belief.probabilities)
result += probEntry.second * pomdp.getUniqueRewardModel().getTotalStateActionReward(probEntry.first, action, pomdp.getTransitionMatrix());
}
return result;
}

8
src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.h
View File

@ -200,8 +200,8 @@ namespace storm {
* @param gridResolution
* @return
*/
std::pair<std::vector<std::vector<ValueType>>, std::vector<ValueType>>
computeSubSimplexAndLambdas(std::vector<ValueType> &probabilities, uint64_t gridResolution);
std::pair<std::vector<std::map<uint64_t, ValueType>>, std::vector<ValueType>>
computeSubSimplexAndLambdas(std::map<uint64_t, ValueType> &probabilities, uint64_t gridResolution, uint64_t nrStates);
/**
@ -269,7 +269,7 @@ namespace storm {
* @return
*/
uint64_t getBeliefIdInVector(std::vector<storm::pomdp::Belief<ValueType>> const &grid, uint32_t observation,
std::vector<ValueType> &probabilities);
std::map<uint64_t, ValueType> &probabilities);
/**
* @param transitions data structure that contains the transition information of the form: origin-state -> action -> (successor-state -> probability)
@ -285,7 +285,7 @@ namespace storm {
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, std::vector<ValueType>> &beliefActionRewards,
std::map<uint64_t, std::vector<std::vector<ValueType>>> &subSimplexCache,
std::map<uint64_t, std::vector<std::map<uint64_t, ValueType>>> &subSimplexCache,
std::map<uint64_t, std::vector<ValueType>> &lambdaCache, std::map<uint64_t, ValueType> &result,
std::map<uint64_t, std::vector<uint64_t>> &chosenActions,
uint64_t gridResolution, bool min, bool computeRewards);

2
src/storm-pomdp/storage/Belief.h
View File

@ -6,7 +6,7 @@ namespace storm {
uint64_t id;
uint32_t observation;
//TODO make this sparse?
std::vector<ValueType> probabilities;
std::map<uint64_t, ValueType> probabilities;
};
}
}
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