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Implemented triangulation in a more dynamic way.

main
Tim Quatmann 5 years ago
parent
cc5faee9c0
commit
fa10087fba
3 changed files with 112 additions and 58 deletions
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  1. 1
      src/storm-pomdp/modelchecker/ApproximatePOMDPModelCheckerOptions.h
  2. 8
      src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.cpp
  3. 75
      src/storm-pomdp/storage/BeliefManager.h

1
src/storm-pomdp/modelchecker/ApproximatePOMDPModelCheckerOptions.h
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@ -38,6 +38,7 @@ namespace storm {
ValueType obsThresholdIncrementFactor = storm::utility::convertNumber<ValueType>(0.1);
ValueType numericPrecision = storm::NumberTraits<ValueType>::IsExact ? storm::utility::zero<ValueType>() : storm::utility::convertNumber<ValueType>(1e-9); /// Used to decide whether two beliefs are equal
bool dynamicTriangulation = true; // Sets whether the triangulation is done in a dynamic way (yielding more precise triangulations)
};
}
}

8
src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.cpp
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@ -185,7 +185,7 @@ namespace storm {
if (options.discretize) {
std::vector<uint64_t> observationResolutionVector(pomdp.getNrObservations(), options.resolutionInit);
auto manager = std::make_shared<BeliefManagerType>(pomdp, options.numericPrecision);
auto manager = std::make_shared<BeliefManagerType>(pomdp, options.numericPrecision, options.dynamicTriangulation ? BeliefManagerType::TriangulationMode::Dynamic : BeliefManagerType::TriangulationMode::Static);
if (rewardModelName) {
manager->setRewardModel(rewardModelName);
}
@ -205,7 +205,7 @@ namespace storm {
}
}
if (options.unfold) { // Underapproximation (uses a fresh Belief manager)
auto manager = std::make_shared<BeliefManagerType>(pomdp, options.numericPrecision);
auto manager = std::make_shared<BeliefManagerType>(pomdp, options.numericPrecision, options.dynamicTriangulation ? BeliefManagerType::TriangulationMode::Dynamic : BeliefManagerType::TriangulationMode::Static);
if (rewardModelName) {
manager->setRewardModel(rewardModelName);
}
@ -243,7 +243,7 @@ namespace storm {
HeuristicParameters overApproxHeuristicPar;
if (options.discretize) { // Setup and build first OverApproximation
observationResolutionVector = std::vector<uint64_t>(pomdp.getNrObservations(), options.resolutionInit);
overApproxBeliefManager = std::make_shared<BeliefManagerType>(pomdp, options.numericPrecision);
overApproxBeliefManager = std::make_shared<BeliefManagerType>(pomdp, options.numericPrecision, options.dynamicTriangulation ? BeliefManagerType::TriangulationMode::Dynamic : BeliefManagerType::TriangulationMode::Static);
if (rewardModelName) {
overApproxBeliefManager->setRewardModel(rewardModelName);
}
@ -267,7 +267,7 @@ namespace storm {
std::shared_ptr<ExplorerType> underApproximation;
HeuristicParameters underApproxHeuristicPar;
if (options.unfold) { // Setup and build first UnderApproximation
underApproxBeliefManager = std::make_shared<BeliefManagerType>(pomdp, options.numericPrecision);
underApproxBeliefManager = std::make_shared<BeliefManagerType>(pomdp, options.numericPrecision, options.dynamicTriangulation ? BeliefManagerType::TriangulationMode::Dynamic : BeliefManagerType::TriangulationMode::Static);
if (rewardModelName) {
underApproxBeliefManager->setRewardModel(rewardModelName);
}

75
src/storm-pomdp/storage/BeliefManager.h
View File

@ -20,7 +20,12 @@ namespace storm {
typedef boost::container::flat_set<StateType> BeliefSupportType;
typedef uint64_t BeliefId;
BeliefManager(PomdpType const& pomdp, BeliefValueType const& precision) : pomdp(pomdp), cc(precision, false) {
enum class TriangulationMode {
Static,
Dynamic
};
BeliefManager(PomdpType const& pomdp, BeliefValueType const& precision, TriangulationMode const& triangulationMode) : pomdp(pomdp), cc(precision, false), triangulationMode(triangulationMode) {
beliefToIdMap.resize(pomdp.getNrObservations());
initialBeliefId = computeInitialBelief();
}
@ -288,19 +293,12 @@ namespace storm {
}
};
Triangulation triangulateBelief(BeliefType belief, uint64_t resolution) {
STORM_LOG_ASSERT(assertBelief(belief), "Input belief for triangulation is not valid.");
void triangulateBeliefFreudenthal(BeliefType const& belief, uint64_t const& resolution, Triangulation& result) {
STORM_LOG_ASSERT(resolution != 0, "Invalid resolution: 0");
StateType numEntries = belief.size();
Triangulation result;
// Quickly triangulate Dirac beliefs
if (numEntries == 1u) {
result.weights.push_back(storm::utility::one<BeliefValueType>());
result.gridPoints.push_back(getOrAddBeliefId(belief));
} else {
auto convResolution = storm::utility::convertNumber<BeliefValueType>(resolution);
// This is the Freudenthal Triangulation as described in Lovejoy (a whole lotta math)
// Probabilities will be triangulated to values in 0/N, 1/N, 2/N, ..., N/N
// Variable names are mostly based on the paper
// However, we speed this up a little by exploiting that belief states usually have sparse support (i.e. numEntries is much smaller than pomdp.getNumberOfStates()).
// Initialize diffs and the first row of the 'qs' matrix (aka v)
@ -349,6 +347,60 @@ namespace storm {
previousSortedDiff = currentSortedDiff++;
}
}
void triangulateBeliefDynamic(BeliefType const& belief, uint64_t const& resolution, Triangulation& result) {
// Find the best resolution for this belief, i.e., N such that the largest distance between one of the belief values to a value in {i/N | 0 ≤ i ≤ N} is minimal
uint64_t finalResolution = resolution;
BeliefValueType finalResolutionDist = storm::utility::one<BeliefValueType>();
// We don't need to check resolutions that are smaller than the maximal resolution divided by 2 (as we already checked multiples of these)
for (uint64_t currResolution = resolution; currResolution > resolution / 2; --currResolution) {
BeliefValueType currResDist = storm::utility::zero<ValueType>();
BeliefValueType currResolutionConverted = storm::utility::convertNumber<BeliefValueType>(currResolution);
bool continueWithNextResolution = false;
for (auto const& belEntry : belief) {
BeliefValueType product = belEntry.second * currResolutionConverted;
BeliefValueType dist = storm::utility::abs<BeliefValueType>(product - storm::utility::round(product)) / currResolutionConverted;
if (dist > currResDist) {
if (dist > finalResolutionDist) {
// This resolution is worse than a previous resolution
continueWithNextResolution = true;
break;
}
currResDist = dist;
}
}
STORM_LOG_ASSERT(continueWithNextResolution || currResDist <= finalResolutionDist, "Distance for this resolution should not be larger than a previously checked one.");
if (!continueWithNextResolution) {
finalResolution = currResolution;
finalResolutionDist = currResDist;
}
}
STORM_LOG_TRACE("Picking resolution " << finalResolution << " for belief " << toString(belief));
// do standard freudenthal with the found resolution
triangulateBeliefFreudenthal(belief, finalResolution, result);
}
Triangulation triangulateBelief(BeliefType const& belief, uint64_t const& resolution) {
STORM_LOG_ASSERT(assertBelief(belief), "Input belief for triangulation is not valid.");
Triangulation result;
// Quickly triangulate Dirac beliefs
if (belief.size() == 1u) {
result.weights.push_back(storm::utility::one<BeliefValueType>());
result.gridPoints.push_back(getOrAddBeliefId(belief));
} else {
switch (triangulationMode) {
case TriangulationMode::Static:
triangulateBeliefFreudenthal(belief, resolution, result);
break;
case TriangulationMode::Dynamic:
triangulateBeliefDynamic(belief, resolution, result);
break;
default:
STORM_LOG_ASSERT(false, "Invalid triangulation mode.");
}
}
STORM_LOG_ASSERT(assertTriangulation(belief, result), "Incorrect triangulation: " << toString(result));
return result;
}
@ -445,6 +497,7 @@ namespace storm {
storm::utility::ConstantsComparator<ValueType> cc;
TriangulationMode triangulationMode;
};
}
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