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step bounded objectives 

Former-commit-id: 067f4b8dae
main
TimQu 10 years ago
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18c1fc3b3f
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b58c9d67b9
2 changed files with 73 additions and 24 deletions
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  1. 88
      src/modelchecker/multiobjective/helper/SparseMultiObjectiveWeightVectorChecker.cpp
  2. 9
      src/modelchecker/multiobjective/helper/SparseMultiObjectiveWeightVectorChecker.h

88
src/modelchecker/multiobjective/helper/SparseMultiObjectiveWeightVectorChecker.cpp
View File

@ -1,5 +1,7 @@
#include "src/modelchecker/multiobjective/helper/SparseMultiObjectiveWeightVectorChecker.h"
#include <map>
#include "src/adapters/CarlAdapter.h"
#include "src/models/sparse/Mdp.h"
#include "src/models/sparse/StandardRewardModel.h"
@ -12,6 +14,7 @@
#include "src/utility/vector.h"
#include "src/exceptions/IllegalFunctionCallException.h"
#include "src/exceptions/NotImplementedException.h"
namespace storm {
namespace modelchecker {
@ -29,11 +32,19 @@ namespace storm {
void SparseMultiObjectiveWeightVectorChecker<SparseModelType>::check(std::vector<ValueType> const& weightVector) {
checkHasBeenCalled=true;
STORM_LOG_DEBUG("Invoked WeightVectorChecker with weights " << std::endl << "\t" << weightVector);
unboundedWeightedPhase(weightVector);
storm::storage::BitVector unboundedObjectives(data.objectives.size(), false);
std::vector<ValueType> weightedRewardVector(data.preprocessedModel.getTransitionMatrix().getRowCount(), storm::utility::zero<ValueType>());
for(uint_fast64_t objIndex = 0; objIndex < data.objectives.size(); ++objIndex) {
if(!data.objectives[objIndex].stepBound) {
unboundedObjectives.set(objIndex, true);
storm::utility::vector::addScaledVector(weightedRewardVector, data.preprocessedModel.getRewardModel(data.objectives[objIndex].rewardModelName).getStateActionRewardVector(), weightVector[objIndex]);
}
}
unboundedWeightedPhase(unboundedObjectives, weightedRewardVector);
STORM_LOG_DEBUG("Unbounded weighted phase result: " << weightedResult[data.preprocessedModel.getInitialStates().getNextSetIndex(0)] << " (value in initial state).");
unboundedIndividualPhase(weightVector);
STORM_LOG_DEBUG("Unbounded individual phase results in initial state: " << getInitialStateResultOfObjectives());
boundedPhase(weightVector);
boundedPhase(weightVector, ~unboundedObjectives, weightedRewardVector);
STORM_LOG_DEBUG("Bounded individual phase results in initial state: " << getInitialStateResultOfObjectives() << " ...WeightVectorChecker done.");
}
@ -53,29 +64,19 @@ namespace storm {
template <class SparseModelType>
storm::storage::TotalScheduler const& SparseMultiObjectiveWeightVectorChecker<SparseModelType>::getScheduler() const {
STORM_LOG_THROW(checkHasBeenCalled, storm::exceptions::IllegalFunctionCallException, "Tried to retrieve results but check(..) has not been called before.");
for(auto const& obj : data.objectives) {
STORM_LOG_THROW(!obj.stepBound, storm::exceptions::NotImplementedException, "Scheduler retrival is not implemented for stepbounded objectives.");
}
return scheduler;
}
template <class SparseModelType>
void SparseMultiObjectiveWeightVectorChecker<SparseModelType>::unboundedWeightedPhase(std::vector<ValueType> const& weightVector) {
bool hasUnboundedObjective;
std::vector<ValueType> weightedRewardVector(data.preprocessedModel.getTransitionMatrix().getRowCount(), storm::utility::zero<ValueType>());
for(uint_fast64_t objIndex = 0; objIndex < weightVector.size(); ++objIndex) {
if(!data.objectives[objIndex].stepBound){
hasUnboundedObjective = true;
storm::utility::vector::addScaledVector(weightedRewardVector, data.preprocessedModel.getRewardModel(data.objectives[objIndex].rewardModelName).getStateActionRewardVector(), weightVector[objIndex]);
}
}
if(!hasUnboundedObjective) {
void SparseMultiObjectiveWeightVectorChecker<SparseModelType>::unboundedWeightedPhase(storm::storage::BitVector const& unboundedObjectives, std::vector<ValueType> const& weightedRewardVector) {
if(unboundedObjectives.empty()) {
this->weightedResult = std::vector<ValueType>(data.preprocessedModel.getNumberOfStates(), storm::utility::zero<ValueType>());
this->scheduler = storm::storage::TotalScheduler(data.preprocessedModel.getNumberOfStates());
return;
}
// TODO check for +/- infty reward...
//std::cout << "weighted reward vector is " << storm::utility::vector::toString(weightedRewardVector) << std::endl;
// Remove end components in which no reward is earned
auto removerResult = storm::transformer::NeutralECRemover<ValueType>::transform(data.preprocessedModel.getTransitionMatrix(), weightedRewardVector, storm::storage::BitVector(data.preprocessedModel.getTransitionMatrix().getRowGroupCount(), true));
@ -141,7 +142,7 @@ namespace storm {
//TODO check if all but one entry of weightVector is zero
//Also only compute values for objectives with weightVector != zero,
//one check can be omitted as the result can be computed back from the weighed result and the results from the remaining objectives
for(uint_fast64_t objIndex = 0; objIndex < weightVector.size(); ++objIndex) {
for(uint_fast64_t objIndex = 0; objIndex < data.objectives.size(); ++objIndex) {
if(data.objectives[objIndex].stepBound){
objectiveResults[objIndex] = std::vector<ValueType>(data.preprocessedModel.getNumberOfStates(), storm::utility::zero<ValueType>());
} else {
@ -162,9 +163,54 @@ namespace storm {
}
template <class SparseModelType>
void SparseMultiObjectiveWeightVectorChecker<SparseModelType>::boundedPhase(std::vector<ValueType> const& weightVector) {
for(uint_fast64_t objIndex = 0; objIndex < weightVector.size(); ++objIndex) {
STORM_LOG_THROW(!data.objectives[objIndex].stepBound, storm::exceptions::IllegalFunctionCallException, "Bounded objectives not yet implemented.");
void SparseMultiObjectiveWeightVectorChecker<SparseModelType>::boundedPhase(std::vector<ValueType> const& weightVector, storm::storage::BitVector const& boundedObjectives, std::vector<ValueType>& weightedRewardVector) {
if(boundedObjectives.empty()) {
return;
}
// Allocate some memory so this does not need to happen for each time epoch
std::vector<uint_fast64_t> optimalChoicesInCurrentEpoch(data.preprocessedModel.getNumberOfStates());
std::vector<ValueType> choiceValues(weightedRewardVector.size());
std::vector<ValueType> temporaryResult(data.preprocessedModel.getNumberOfStates());
// Get for each occurring stepBound the indices of the objectives with that bound.
std::map<uint_fast64_t, storm::storage::BitVector, std::greater<uint_fast64_t>> stepBounds;
for(auto objIndex : boundedObjectives) {
auto stepBoundIt = stepBounds.insert(std::make_pair(*data.objectives[objIndex].stepBound, storm::storage::BitVector(data.objectives.size(), false))).first;
stepBoundIt->second.set(objIndex);
}
storm::storage::BitVector objectivesAtCurrentEpoch = ~boundedObjectives;
auto stepBoundIt = stepBounds.begin();
for(uint_fast64_t currentEpoch = stepBoundIt->first; currentEpoch > 0; --currentEpoch) {
if(stepBoundIt != stepBounds.end() && currentEpoch == stepBoundIt->first) {
objectivesAtCurrentEpoch |= stepBoundIt->second;
for(auto objIndex : stepBoundIt->second) {
storm::utility::vector::addScaledVector(weightedRewardVector, data.preprocessedModel.getRewardModel(data.objectives[objIndex].rewardModelName).getStateActionRewardVector(), weightVector[objIndex]);
}
++stepBoundIt;
}
// Get values and scheduler for weighted sum of objectives
data.preprocessedModel.getTransitionMatrix().multiplyWithVector(weightedResult, choiceValues);
storm::utility::vector::addVectors(choiceValues, weightedRewardVector, choiceValues);
storm::utility::vector::reduceVectorMax(choiceValues, weightedResult, data.preprocessedModel.getTransitionMatrix().getRowGroupIndices(), &optimalChoicesInCurrentEpoch);
// get values for individual objectives
// TODO we could compute the result for one of the objectives from the weighted result, the given weight vector, and the remaining objective results.
for(auto objIndex : objectivesAtCurrentEpoch) {
std::vector<ValueType>& objectiveResult = objectiveResults[objIndex];
std::vector<ValueType> const& objectiveRewards = data.preprocessedModel.getRewardModel(data.objectives[objIndex].rewardModelName).getStateActionRewardVector();
auto rowGroupIndexIt = data.preprocessedModel.getTransitionMatrix().getRowGroupIndices().begin();
auto optimalChoiceIt = optimalChoicesInCurrentEpoch.begin();
for(ValueType& stateValue : temporaryResult){
uint_fast64_t row = (*rowGroupIndexIt) + (*optimalChoiceIt);
++rowGroupIndexIt;
++optimalChoiceIt;
stateValue = objectiveRewards[row];
for(auto const& entry : data.preprocessedModel.getTransitionMatrix().getRow(row)) {
stateValue += entry.getValue() * objectiveResult[entry.getColumn()];
}
}
objectiveResult.swap(temporaryResult);
}
}
}

9
src/modelchecker/multiobjective/helper/SparseMultiObjectiveWeightVectorChecker.h
View File

@ -48,9 +48,10 @@ namespace storm {
/*!
* Determines the scheduler that maximizes the weighted reward vector of the unbounded objectives
*
* @param weightVector the weight vector of the current check
* @param unboundedObjectives the objectives without a stepBound
* @param weightedRewardVector the weighted rewards (only considering the unbounded objectives)
*/
void unboundedWeightedPhase(std::vector<ValueType> const& weightVector);
void unboundedWeightedPhase(storm::storage::BitVector const& unboundedObjectives, std::vector<ValueType> const& weightedRewardVector);
/*!
* Computes the values of the objectives that do not have a stepBound w.r.t. the scheduler computed in the unboundedWeightedPhase
@ -66,8 +67,10 @@ namespace storm {
* - computes the values of these objectives w.r.t. this scheduler
*
* @param weightVector the weight vector of the current check
* @param boundedObjectives the objectives with a stepBound
* @param weightedRewardVector the weighted rewards (initially only considering the unbounded objectives, will be extended to all objectives)
*/
void boundedPhase(std::vector<ValueType> const& weightVector);
void boundedPhase(std::vector<ValueType> const& weightVector, storm::storage::BitVector const& unboundedObjectives, std::vector<ValueType>& weightedRewardVector);
// stores the considered information of the multi-objective model checking problem
PreprocessorData const& data;

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