@ -15,13 +15,8 @@
# include "storm/models/sparse/StandardRewardModel.h"
# include "storm/modelchecker/prctl/SparseDtmcPrctlModelChecker.h"
# include "storm/utility/vector.h"
# include "storm/modelchecker/results/CheckResult.h"
# include "storm/modelchecker/results/ExplicitQualitativeCheckResult.h"
# include "storm/modelchecker/results/ExplicitQuantitativeCheckResult.h"
# include "storm/modelchecker/hints/ExplicitModelCheckerHint.cpp"
# include "storm/api/properties.h"
# include "storm/api/export.h"
# include "storm-parsers/api/storm-parsers.h"
# include "storm-pomdp/builder/BeliefMdpExplorer.h"
# include "storm-pomdp/modelchecker/TrivialPomdpValueBoundsModelChecker.h"
@ -32,8 +27,8 @@
namespace storm {
namespace pomdp {
namespace modelchecker {
template < typename ValueType , typename RewardModel Type>
ApproximatePOMDPModelchecker < ValueType , RewardModel Type> : : Options : : Options ( ) {
template < typename PomdpModelType , typename BeliefValue Type>
ApproximatePOMDPModelchecker < PomdpModelType , BeliefValue Type> : : Options : : Options ( ) {
initialGridResolution = 10 ;
explorationThreshold = storm : : utility : : zero < ValueType > ( ) ;
doRefinement = true ;
@ -41,52 +36,69 @@ namespace storm {
numericPrecision = storm : : NumberTraits < ValueType > : : IsExact ? storm : : utility : : zero < ValueType > ( ) : storm : : utility : : convertNumber < ValueType > ( 1e-9 ) ;
cacheSubsimplices = false ;
}
template < typename ValueType , typename RewardModelType >
ApproximatePOMDPModelchecker < ValueType , RewardModelType > : : Statistics : : Statistics ( ) : overApproximationBuildAborted ( false ) , underApproximationBuildAborted ( false ) , aborted ( false ) {
template < typename PomdpModelType , typename BeliefValueType >
ApproximatePOMDPModelchecker < PomdpModelType , BeliefValueType > : : Result : : Result ( ValueType lower , ValueType upper ) : lowerBound ( lower ) , upperBound ( upper ) {
// Intentionally left empty
}
template < typename PomdpModelType , typename BeliefValueType >
typename ApproximatePOMDPModelchecker < PomdpModelType , BeliefValueType > : : ValueType
ApproximatePOMDPModelchecker < PomdpModelType , BeliefValueType > : : Result : : diff ( bool relative ) const {
ValueType diff = upperBound - lowerBound ;
if ( diff < storm : : utility : : zero < ValueType > ( ) ) {
STORM_LOG_WARN_COND ( diff > = 1e-6 , " Upper bound ' " < < upperBound < < " ' is smaller than lower bound ' " < < lowerBound < < " ': Difference is " < < diff < < " . " ) ;
diff = storm : : utility : : zero < ValueType > ( ) ;
}
if ( relative & & ! storm : : utility : : isZero ( upperBound ) ) {
diff / = upperBound ;
}
return diff ;
}
template < typename PomdpModelType , typename BeliefValueType >
ApproximatePOMDPModelchecker < PomdpModelType , BeliefValueType > : : Statistics : : Statistics ( ) : overApproximationBuildAborted ( false ) , underApproximationBuildAborted ( false ) , aborted ( false ) {
// intentionally left empty;
}
template < typename ValueType , typename RewardModelType >
ApproximatePOMDPModelchecker < ValueType , RewardModelType > : : ApproximatePOMDPModelchecker ( storm : : models : : sparse : : Pomdp < ValueType , RewardModelType > const & pomdp , Options options ) : pomdp ( pomdp ) , options ( options ) {
template < typename PomdpModelType , typename BeliefValue Type>
ApproximatePOMDPModelchecker < PomdpModelType , BeliefValue Type> : : ApproximatePOMDPModelchecker ( PomdpModelType const & pomdp , Options options ) : pomdp ( pomdp ) , options ( options ) {
cc = storm : : utility : : ConstantsComparator < ValueType > ( storm : : utility : : convertNumber < ValueType > ( this - > options . numericPrecision ) , false ) ;
}
template < typename ValueType , typename RewardModelType >
std : : unique_ptr < POMDPCheckResult < ValueType > > ApproximatePOMDPModelchecker < ValueType , RewardModelType > : : check ( storm : : logic : : Formula const & formula ) {
template < typename PomdpModelType , typename BeliefValue Type>
typename ApproximatePOMDPModelchecker < PomdpModelType , BeliefValueType > : : Result ApproximatePOMDPModelchecker < PomdpModelType , BeliefValue Type> : : check ( storm : : logic : : Formula const & formula ) {
// Reset all collected statistics
statistics = Statistics ( ) ;
std : : unique_ptr < POMDPCheckResult < ValueType > > result ;
// Extract the relevant information from the formula
auto formulaInfo = storm : : pomdp : : analysis : : getFormulaInformation ( pomdp , formula ) ;
// Compute some initial bounds on the values for each state of the pomdp
auto initialPomdpValueBounds = TrivialPomdpValueBoundsModelChecker < storm : : models : : sparse : : Pomdp < ValueType > > ( pomdp ) . getValueBounds ( formula , formulaInfo ) ;
Result result ( initialPomdpValueBounds . lower [ pomdp . getInitialStates ( ) . getNextSetIndex ( 0 ) ] , initialPomdpValueBounds . upper [ pomdp . getInitialStates ( ) . getNextSetIndex ( 0 ) ] ) ;
if ( formulaInfo . isNonNestedReachabilityProbability ( ) ) {
boost : : optional < std : : string > rewardModelName ;
if ( formulaInfo . isNonNestedReachabilityProbability ( ) | | formulaInfo . isNonNestedExpectedRewardFormula ( ) ) {
// FIXME: Instead of giving up, introduce a new observation for target states and make sink states absorbing.
STORM_LOG_THROW ( formulaInfo . getTargetStates ( ) . observationClosed , storm : : exceptions : : NotSupportedException , " There are non-target states with the same observation as a target state. This is currently not supported " ) ;
if ( formulaInfo . isNonNestedReachabilityProbability ( ) ) {
if ( ! formulaInfo . getSinkStates ( ) . empty ( ) ) {
auto reachableFromSinkStates = storm : : utility : : graph : : getReachableStates ( pomdp . getTransitionMatrix ( ) , formulaInfo . getSinkStates ( ) . states , formulaInfo . getSinkStates ( ) . states , ~ formulaInfo . getSinkStates ( ) . states ) ;
reachableFromSinkStates & = ~ formulaInfo . getSinkStates ( ) . states ;
STORM_LOG_THROW ( reachableFromSinkStates . empty ( ) , storm : : exceptions : : NotSupportedException , " There are sink states that can reach non-sink states. This is currently not supported " ) ;
}
if ( options . doRefinement ) {
result = refineReachability ( formulaInfo . getTargetStates ( ) . observations , formulaInfo . minimize ( ) , false ) ;
} else {
result = computeReachabilityOTF ( formulaInfo . getTargetStates ( ) . observations , formulaInfo . minimize ( ) , false , initialPomdpValueBounds . lower , initialPomdpValueBounds . upper ) ;
// Expected reward formula!
rewardModelName = formulaInfo . getRewardModelName ( ) ;
}
} else if ( formulaInfo . isNonNestedExpectedRewardFormula ( ) ) {
// FIXME: Instead of giving up, introduce a new observation for target states and make sink states absorbing.
STORM_LOG_THROW ( formulaInfo . getTargetStates ( ) . observationClosed , storm : : exceptions : : NotSupportedException , " There are non-target states with the same observation as a target state. This is currently not supported " ) ;
if ( options . doRefinement ) {
result = refineReachability ( formulaInfo . getTargetStates ( ) . observations , formulaInfo . minimize ( ) , true ) ;
} else {
// FIXME: pick the non-unique reward model here
STORM_LOG_THROW ( pomdp . hasUniqueRewardModel ( ) , storm : : exceptions : : NotSupportedException , " Non-unique reward models not implemented yet. " ) ;
result = computeReachabilityOTF ( formulaInfo . getTargetStates ( ) . observations , formulaInfo . minimize ( ) , true , initialPomdpValueBounds . lower , initialPomdpValueBounds . upper ) ;
STORM_LOG_THROW ( false , storm : : exceptions : : NotSupportedException , " Unsupported formula ' " < < formula < < " '. " ) ;
}
if ( options . doRefinement ) {
refineReachability ( formulaInfo . getTargetStates ( ) . observations , formulaInfo . minimize ( ) , rewardModelName , initialPomdpValueBounds . lower , initialPomdpValueBounds . upper , result ) ;
} else {
STORM_LOG_THROW ( false , storm : : exceptions : : NotSupportedException , " Unsupported formula ' " < < formula < < " '. " ) ;
computeReachabilityOTF ( formulaInfo . getTargetStates ( ) . observations , formulaInfo . minimize ( ) , rewardModelName , initialPomdpValueBounds . lower , initialPomdpValueBounds . upper , result ) ;
}
if ( storm : : utility : : resources : : isTerminate ( ) ) {
statistics . aborted = true ;
@ -94,8 +106,8 @@ namespace storm {
return result ;
}
template < typename ValueType , typename RewardModel Type>
void ApproximatePOMDPModelchecker < ValueType , RewardModel Type> : : printStatisticsToStream ( std : : ostream & stream ) const {
template < typename PomdpModelType , typename BeliefValue Type>
void ApproximatePOMDPModelchecker < PomdpModelType , BeliefValue Type> : : printStatisticsToStream ( std : : ostream & stream ) const {
stream < < " ##### Grid Approximation Statistics ###### " < < std : : endl ;
stream < < " # Input model: " < < std : : endl ;
pomdp . printModelInformationToStream ( stream ) ;
@ -143,114 +155,82 @@ namespace storm {
stream < < " ########################################## " < < std : : endl ;
}
std : : shared_ptr < storm : : logic : : Formula const > createStandardProperty ( bool min , bool computeRewards ) {
std : : string propertyString = computeRewards ? " R " : " P " ;
propertyString + = min ? " min " : " max " ;
propertyString + = " =? [F \" target \" ] " ;
std : : vector < storm : : jani : : Property > propertyVector = storm : : api : : parseProperties ( propertyString ) ;
return storm : : api : : extractFormulasFromProperties ( propertyVector ) . front ( ) ;
template < typename PomdpModelType , typename BeliefValueType >
void ApproximatePOMDPModelchecker < PomdpModelType , BeliefValueType > : : computeReachabilityOTF ( std : : set < uint32_t > const & targetObservations , bool min , boost : : optional < std : : string > rewardModelName , std : : vector < ValueType > const & lowerPomdpValueBounds , std : : vector < ValueType > const & upperPomdpValueBounds , Result & result ) {
if ( options . explorationThreshold > storm : : utility : : zero < ValueType > ( ) ) {
STORM_PRINT ( " Exploration threshold: " < < options . explorationThreshold < < std : : endl )
}
template < typename ValueType >
storm : : modelchecker : : CheckTask < storm : : logic : : Formula , ValueType > createStandardCheckTask ( std : : shared_ptr < storm : : logic : : Formula const > & property , std : : vector < ValueType > & & hintVector ) {
//Note: The property should not run out of scope after calling this because the task only stores the property by reference.
// Therefore, this method needs the property by reference (and not const reference)
auto task = storm : : api : : createTask < ValueType > ( property , false ) ;
if ( ! hintVector . empty ( ) ) {
auto hint = storm : : modelchecker : : ExplicitModelCheckerHint < ValueType > ( ) ;
hint . setResultHint ( std : : move ( hintVector ) ) ;
auto hintPtr = std : : make_shared < storm : : modelchecker : : ExplicitModelCheckerHint < ValueType > > ( hint ) ;
task . setHint ( hintPtr ) ;
}
return task ;
}
template < typename ValueType , typename RewardModelType >
std : : unique_ptr < POMDPCheckResult < ValueType > >
ApproximatePOMDPModelchecker < ValueType , RewardModelType > : : refineReachability ( std : : set < uint32_t > const & targetObservations , bool min , bool computeRewards ) {
std : : srand ( time ( NULL ) ) ;
// Compute easy upper and lower bounds
storm : : utility : : Stopwatch underlyingWatch ( true ) ;
// Compute the results on the underlying MDP as a basic overapproximation
storm : : models : : sparse : : StateLabeling underlyingMdpLabeling ( pomdp . getStateLabeling ( ) ) ;
// TODO: Is the following really necessary
underlyingMdpLabeling . addLabel ( " __goal__ " ) ;
std : : vector < uint64_t > goalStates ;
for ( auto const & targetObs : targetObservations ) {
for ( auto const & goalState : pomdp . getStatesWithObservation ( targetObs ) ) {
underlyingMdpLabeling . addLabelToState ( " __goal__ " , goalState ) ;
}
}
storm : : models : : sparse : : Mdp < ValueType , RewardModelType > underlyingMdp ( pomdp . getTransitionMatrix ( ) , underlyingMdpLabeling , pomdp . getRewardModels ( ) ) ;
auto underlyingModel = std : : static_pointer_cast < storm : : models : : sparse : : Model < ValueType , RewardModelType > > (
std : : make_shared < storm : : models : : sparse : : Mdp < ValueType , RewardModelType > > ( underlyingMdp ) ) ;
std : : string initPropString = computeRewards ? " R " : " P " ;
initPropString + = min ? " min " : " max " ;
initPropString + = " =? [F \" __goal__ \" ] " ;
std : : vector < storm : : jani : : Property > propVector = storm : : api : : parseProperties ( initPropString ) ;
std : : shared_ptr < storm : : logic : : Formula const > underlyingProperty = storm : : api : : extractFormulasFromProperties ( propVector ) . front ( ) ;
STORM_PRINT ( " Underlying MDP " < < std : : endl )
if ( computeRewards ) {
underlyingMdp . addRewardModel ( " std " , pomdp . getUniqueRewardModel ( ) ) ;
}
underlyingMdp . printModelInformationToStream ( std : : cout ) ;
std : : unique_ptr < storm : : modelchecker : : CheckResult > underlyingRes (
storm : : api : : verifyWithSparseEngine < ValueType > ( underlyingModel , storm : : api : : createTask < ValueType > ( underlyingProperty , false ) ) ) ;
STORM_LOG_ASSERT ( underlyingRes , " Result not exist. " ) ;
underlyingRes - > filter ( storm : : modelchecker : : ExplicitQualitativeCheckResult ( storm : : storage : : BitVector ( underlyingMdp . getNumberOfStates ( ) , true ) ) ) ;
auto initialOverApproxMap = underlyingRes - > asExplicitQuantitativeCheckResult < ValueType > ( ) . getValueMap ( ) ;
underlyingWatch . stop ( ) ;
storm : : utility : : Stopwatch positionalWatch ( true ) ;
// we define some positional scheduler for the POMDP as a basic lower bound
storm : : storage : : Scheduler < ValueType > pomdpScheduler ( pomdp . getNumberOfStates ( ) ) ;
for ( uint32_t obs = 0 ; obs < pomdp . getNrObservations ( ) ; + + obs ) {
auto obsStates = pomdp . getStatesWithObservation ( obs ) ;
// select a random action for all states with the same observation
uint64_t chosenAction = std : : rand ( ) % pomdp . getNumberOfChoices ( obsStates . front ( ) ) ;
for ( auto const & state : obsStates ) {
pomdpScheduler . setChoice ( chosenAction , state ) ;
}
}
auto underApproxModel = underlyingMdp . applyScheduler ( pomdpScheduler , false ) ;
if ( computeRewards ) {
underApproxModel - > restrictRewardModels ( { " std " } ) ;
}
STORM_PRINT ( " Random Positional Scheduler " < < std : : endl )
underApproxModel - > printModelInformationToStream ( std : : cout ) ;
std : : unique_ptr < storm : : modelchecker : : CheckResult > underapproxRes (
storm : : api : : verifyWithSparseEngine < ValueType > ( underApproxModel , storm : : api : : createTask < ValueType > ( underlyingProperty , false ) ) ) ;
STORM_LOG_ASSERT ( underapproxRes , " Result not exist. " ) ;
underapproxRes - > filter ( storm : : modelchecker : : ExplicitQualitativeCheckResult ( storm : : storage : : BitVector ( underApproxModel - > getNumberOfStates ( ) , true ) ) ) ;
auto initialUnderApproxMap = underapproxRes - > asExplicitQuantitativeCheckResult < ValueType > ( ) . getValueMap ( ) ;
positionalWatch . stop ( ) ;
STORM_PRINT ( " Pre-Processing Results: " < < initialOverApproxMap [ underlyingMdp . getInitialStates ( ) . getNextSetIndex ( 0 ) ] < < " // "
< < initialUnderApproxMap [ underApproxModel - > getInitialStates ( ) . getNextSetIndex ( 0 ) ] < < std : : endl )
STORM_PRINT ( " Preprocessing Times: " < < underlyingWatch < < " / " < < positionalWatch < < std : : endl )
// Initialize the resolution mapping. For now, we always give all beliefs with the same observation the same resolution.
// This can probably be improved (i.e. resolutions for single belief states)
STORM_PRINT ( " Initial Resolution: " < < options . initialGridResolution < < std : : endl )
uint64_t underApproxSizeThreshold = 0 ;
{ // Overapproximation
std : : vector < uint64_t > observationResolutionVector ( pomdp . getNrObservations ( ) , options . initialGridResolution ) ;
std : : set < uint32_t > changedObservations ;
uint64_t underApproxModelSize = 200 ;
uint64_t refinementCounter = 1 ;
STORM_PRINT ( " ============================== " < < std : : endl < < " Initial Computation " < < std : : endl < < " ------------------------------ " < < std : : endl )
std : : shared_ptr < RefinementComponents < ValueType > > res = computeFirstRefinementStep ( targetObservations , min , observationResolutionVector , computeRewards ,
{ } ,
{ } , underApproxModelSize ) ;
if ( res = = nullptr ) {
statistics . refinementSteps = 0 ;
return nullptr ;
auto manager = std : : make_shared < BeliefManagerType > ( pomdp , options . numericPrecision ) ;
if ( rewardModelName ) {
manager - > setRewardModel ( rewardModelName ) ;
}
auto approx = computeOverApproximation ( targetObservations , min , rewardModelName . is_initialized ( ) , lowerPomdpValueBounds , upperPomdpValueBounds , observationResolutionVector , manager ) ;
if ( approx ) {
STORM_PRINT_AND_LOG ( " Explored and checked Over-Approximation MDP: \n " ) ;
approx - > getExploredMdp ( ) - > printModelInformationToStream ( std : : cout ) ;
ValueType & resultValue = min ? result . lowerBound : result . upperBound ;
resultValue = approx - > getComputedValueAtInitialState ( ) ;
underApproxSizeThreshold = approx - > getExploredMdp ( ) - > getNumberOfStates ( ) ;
}
}
{ // Underapproximation (Uses a fresh Belief manager)
auto manager = std : : make_shared < BeliefManagerType > ( pomdp , options . numericPrecision ) ;
if ( rewardModelName ) {
manager - > setRewardModel ( rewardModelName ) ;
}
auto approx = computeUnderApproximation ( targetObservations , min , rewardModelName . is_initialized ( ) , lowerPomdpValueBounds , upperPomdpValueBounds , underApproxSizeThreshold , manager ) ;
if ( approx ) {
STORM_PRINT_AND_LOG ( " Explored and checked Under-Approximation MDP: \n " ) ;
approx - > getExploredMdp ( ) - > printModelInformationToStream ( std : : cout ) ;
ValueType & resultValue = min ? result . upperBound : result . lowerBound ;
resultValue = approx - > getComputedValueAtInitialState ( ) ;
}
}
}
ValueType lastMinScore = storm : : utility : : infinity < ValueType > ( ) ;
while ( refinementCounter < 1000 & & ( ( ! min & & res - > overApproxValue - res - > underApproxValue > options . refinementPrecision ) | |
( min & & res - > underApproxValue - res - > overApproxValue > options . refinementPrecision ) ) ) {
template < typename PomdpModelType , typename BeliefValueType >
void ApproximatePOMDPModelchecker < PomdpModelType , BeliefValueType > : : refineReachability ( std : : set < uint32_t > const & targetObservations , bool min , boost : : optional < std : : string > rewardModelName , std : : vector < ValueType > const & lowerPomdpValueBounds , std : : vector < ValueType > const & upperPomdpValueBounds , Result & result ) {
// Set up exploration data
std : : vector < uint64_t > observationResolutionVector ( pomdp . getNrObservations ( ) , options . initialGridResolution ) ;
auto beliefManager = std : : make_shared < BeliefManagerType > ( pomdp , options . numericPrecision ) ;
if ( rewardModelName ) {
beliefManager - > setRewardModel ( rewardModelName ) ;
}
// OverApproximaion
auto overApproximation = computeOverApproximation ( targetObservations , min , rewardModelName . is_initialized ( ) , lowerPomdpValueBounds , upperPomdpValueBounds , observationResolutionVector , beliefManager ) ;
if ( ! overApproximation ) {
return ;
}
ValueType & overApproxValue = min ? result . lowerBound : result . upperBound ;
overApproxValue = overApproximation - > getComputedValueAtInitialState ( ) ;
// UnderApproximation TODO: use same belief manager?)
uint64_t underApproxSizeThreshold = overApproximation - > getExploredMdp ( ) - > getNumberOfStates ( ) ;
auto underApproximation = computeUnderApproximation ( targetObservations , min , rewardModelName . is_initialized ( ) , lowerPomdpValueBounds , upperPomdpValueBounds , underApproxSizeThreshold , beliefManager ) ;
if ( ! underApproximation ) {
return ;
}
ValueType & underApproxValue = min ? result . upperBound : result . lowerBound ;
underApproxValue = underApproximation - > getComputedValueAtInitialState ( ) ;
// ValueType lastMinScore = storm::utility::infinity<ValueType>();
// Start refinement
statistics . refinementSteps = 0 ;
while ( result . diff ( ) > options . refinementPrecision ) {
if ( storm : : utility : : resources : : isTerminate ( ) ) {
break ;
}
// TODO the actual refinement
/*
// choose which observation(s) to refine
std : : vector < ValueType > obsAccumulator ( pomdp . getNrObservations ( ) , storm : : utility : : zero < ValueType > ( ) ) ;
std : : vector < uint64_t > beliefCount ( pomdp . getNrObservations ( ) , 0 ) ;
@ -286,9 +266,9 @@ namespace storm {
}
/* for (uint64_t i = 0; i < obsAccumulator.size(); ++i) {
obsAccumulator [ i ] / = storm : : utility : : convertNumber < ValueType > ( beliefCount [ i ] ) ;
} */
// for (uint64_t i = 0; i < obsAccumulator.size(); ++i) {
// obsAccumulator[i] /= storm::utility::convertNumber<ValueType>(beliefCount[i]);
//}
changedObservations . clear ( ) ;
//TODO think about some other scoring methods
@ -301,21 +281,21 @@ namespace storm {
observationResolutionVector [ i ] = maxRes + 1 ;
changedObservations . insert ( i ) ;
}
/* } else {
lastMinScore = std : : min ( maxAvgDifference , lastMinScore ) ;
STORM_PRINT ( " Max Score: " < < maxAvgDifference < < std : : endl )
STORM_PRINT ( " Last Min Score: " < < lastMinScore < < std : : endl )
//STORM_PRINT("Obs(beliefCount): Score " << std::endl << "-------------------------------------" << std::endl)
for ( uint64_t i = 0 ; i < pomdp . getNrObservations ( ) ; + + i ) {
// } else {
// lastMinScore = std::min(maxAvgDifference, lastMinScore);
// STORM_PRINT("Max Score: " << maxAvgDifference << std::endl)
// STORM_PRINT("Last Min Score: " << lastMinScore << std::endl)
// //STORM_PRINT("Obs(beliefCount): Score " << std::endl << "-------------------------------------" << std::endl)
// for (uint64_t i = 0; i < pomdp.getNrObservations(); ++i) {
//STORM_PRINT(i << "(" << beliefCount[i] << "): " << obsAccumulator[i])
if ( cc . isEqual ( obsAccumulator [ i ] , maxAvgDifference ) ) {
// if (cc.isEqual(obsAccumulator[i], maxAvgDifference)) {
//STORM_PRINT(" *** ")
observationResolutionVector [ i ] + = 1 ;
changedObservations . insert ( i ) ;
}
// observationResolutionVector[i] += 1;
// changedObservations.insert(i);
// }
//STORM_PRINT(std::endl)
}
} */
// }
//}
if ( underApproxModelSize < std : : numeric_limits < uint64_t > : : max ( ) - 101 ) {
underApproxModelSize + = 100 ;
}
@ -327,60 +307,13 @@ namespace storm {
STORM_LOG_ERROR_COND ( ( ! min & & cc . isLess ( res - > underApproxValue , res - > overApproxValue ) ) | | ( min & & cc . isLess ( res - > overApproxValue , res - > underApproxValue ) ) | |
cc . isEqual ( res - > underApproxValue , res - > overApproxValue ) ,
" The value for the under-approximation is larger than the value for the over-approximation. " ) ;
+ + refinementCounter ;
}
statistics . refinementSteps = refinementCounter ;
if ( min ) {
return std : : make_unique < POMDPCheckResult < ValueType > > ( POMDPCheckResult < ValueType > { res - > underApproxValue , res - > overApproxValue } ) ;
} else {
return std : : make_unique < POMDPCheckResult < ValueType > > ( POMDPCheckResult < ValueType > { res - > overApproxValue , res - > underApproxValue } ) ;
}
}
template < typename ValueType , typename RewardModelType >
std : : unique_ptr < POMDPCheckResult < ValueType > >
ApproximatePOMDPModelchecker < ValueType , RewardModelType > : : computeReachabilityOTF ( std : : set < uint32_t > const & targetObservations , bool min ,
bool computeRewards ,
std : : vector < ValueType > const & lowerPomdpValueBounds ,
std : : vector < ValueType > const & upperPomdpValueBounds ,
uint64_t maxUaModelSize ) {
STORM_PRINT ( " Use On-The-Fly Grid Generation " < < std : : endl )
std : : vector < uint64_t > observationResolutionVector ( pomdp . getNrObservations ( ) , options . initialGridResolution ) ;
auto result = computeFirstRefinementStep ( targetObservations , min , observationResolutionVector , computeRewards , lowerPomdpValueBounds ,
upperPomdpValueBounds , maxUaModelSize ) ;
if ( result = = nullptr ) {
return nullptr ;
}
if ( min ) {
return std : : make_unique < POMDPCheckResult < ValueType > > ( POMDPCheckResult < ValueType > { result - > underApproxValue , result - > overApproxValue } ) ;
} else {
return std : : make_unique < POMDPCheckResult < ValueType > > ( POMDPCheckResult < ValueType > { result - > overApproxValue , result - > underApproxValue } ) ;
*/
+ + statistics . refinementSteps . get ( ) ;
}
}
template < typename ValueType , typename BeliefType , typename SummandsType >
ValueType getWeightedSum ( BeliefType const & belief , SummandsType const & summands ) {
ValueType result = storm : : utility : : zero < ValueType > ( ) ;
for ( auto const & entry : belief ) {
result + = storm : : utility : : convertNumber < ValueType > ( entry . second ) * storm : : utility : : convertNumber < ValueType > ( summands . at ( entry . first ) ) ;
}
return result ;
}
template < typename ValueType , typename RewardModelType >
std : : shared_ptr < RefinementComponents < ValueType > >
ApproximatePOMDPModelchecker < ValueType , RewardModelType > : : computeFirstRefinementStep ( std : : set < uint32_t > const & targetObservations , bool min ,
std : : vector < uint64_t > & observationResolutionVector ,
bool computeRewards ,
std : : vector < ValueType > const & lowerPomdpValueBounds ,
std : : vector < ValueType > const & upperPomdpValueBounds ,
uint64_t maxUaModelSize ) {
auto beliefManager = std : : make_shared < storm : : storage : : BeliefManager < storm : : models : : sparse : : Pomdp < ValueType > > > ( pomdp , options . numericPrecision ) ;
if ( computeRewards ) {
beliefManager - > setRewardModel ( ) ; // TODO: get actual name
}
template < typename PomdpModelType , typename BeliefValueType >
std : : shared_ptr < typename ApproximatePOMDPModelchecker < PomdpModelType , BeliefValueType > : : ExplorerType > ApproximatePOMDPModelchecker < PomdpModelType , BeliefValueType > : : computeOverApproximation ( std : : set < uint32_t > const & targetObservations , bool min , bool computeRewards , std : : vector < ValueType > const & lowerPomdpValueBounds , std : : vector < ValueType > const & upperPomdpValueBounds , std : : vector < uint64_t > & observationResolutionVector , std : : shared_ptr < BeliefManagerType > & beliefManager ) {
statistics . overApproximationBuildTime . start ( ) ;
storm : : builder : : BeliefMdpExplorer < storm : : models : : sparse : : Pomdp < ValueType > > explorer ( beliefManager , lowerPomdpValueBounds , upperPomdpValueBounds ) ;
if ( computeRewards ) {
@ -390,9 +323,6 @@ namespace storm {
}
// Expand the beliefs to generate the grid on-the-fly
if ( options . explorationThreshold > storm : : utility : : zero < ValueType > ( ) ) {
STORM_PRINT ( " Exploration threshold: " < < options . explorationThreshold < < std : : endl )
}
while ( explorer . hasUnexploredState ( ) ) {
uint64_t currId = explorer . exploreNextState ( ) ;
@ -445,39 +375,20 @@ namespace storm {
explorer . finishExploration ( ) ;
statistics . overApproximationBuildTime . stop ( ) ;
STORM_PRINT ( " Over Approximation MDP build took " < < statistics . overApproximationBuildTime < < " seconds. " < < std : : endl ) ;
explorer . getExploredMdp ( ) - > printModelInformationToStream ( std : : cout ) ;
statistics . overApproximationCheckTime . start ( ) ;
explorer . computeValuesOfExploredMdp ( min ? storm : : solver : : OptimizationDirection : : Minimize : storm : : solver : : OptimizationDirection : : Maximize ) ;
statistics . overApproximationCheckTime . stop ( ) ;
STORM_PRINT ( " Time Overapproximation: " < < statistics . overApproximationCheckTime < < " seconds. " < < std : : endl ) ;
STORM_PRINT ( " Over-Approximation Result: " < < explorer . getComputedValueAtInitialState ( ) < < std : : endl ) ;
//auto underApprox = weightedSumUnderMap[initialBelief.id];
auto underApproxComponents = computeUnderapproximation ( beliefManager , targetObservations , min , computeRewards , maxUaModelSize , lowerPomdpValueBounds , upperPomdpValueBounds ) ;
if ( storm : : utility : : resources : : isTerminate ( ) & & ! underApproxComponents ) {
// TODO: return other components needed for refinement.
//return std::make_unique<RefinementComponents<ValueType>>(RefinementComponents<ValueType>{modelPtr, overApprox, 0, overApproxResultMap, {}, beliefList, beliefGrid, beliefIsTarget, beliefStateMap, {}, initialBelief.id});
//return std::make_unique<RefinementComponents<ValueType>>(RefinementComponents<ValueType>{modelPtr, overApprox, 0, overApproxResultMap, {}, {}, {}, {}, beliefStateMap, {}, beliefManager->getInitialBelief()});
}
STORM_PRINT ( " Under-Approximation Result: " < < underApproxComponents - > underApproxValue < < std : : endl ) ;
/* TODO: return other components needed for refinement.
return std : : make_unique < RefinementComponents < ValueType > > (
RefinementComponents < ValueType > { modelPtr , overApprox , underApproxComponents - > underApproxValue , overApproxResultMap ,
underApproxComponents - > underApproxMap , beliefList , beliefGrid , beliefIsTarget , beliefStateMap ,
underApproxComponents - > underApproxBeliefStateMap , initialBelief . id } ) ;
*/
return std : : make_unique < RefinementComponents < ValueType > > ( RefinementComponents < ValueType > { explorer . getExploredMdp ( ) , explorer . getComputedValueAtInitialState ( ) , underApproxComponents - > underApproxValue , { } ,
underApproxComponents - > underApproxMap , { } , { } , { } , { } , underApproxComponents - > underApproxBeliefStateMap , beliefManager - > getInitialBelief ( ) } ) ;
return std : : make_shared < ExplorerType > ( std : : move ( explorer ) ) ;
}
template < typename ValueType , typename RewardModelType >
template < typename PomdpModelType , typename BeliefValueType >
void ApproximatePOMDPModelchecker < PomdpModelType , BeliefValueType > : : refineOverApproximation ( std : : set < uint32_t > const & targetObservations , bool min , bool computeRewards , std : : vector < uint64_t > & observationResolutionVector , std : : shared_ptr < BeliefManagerType > & beliefManager , std : : shared_ptr < ExplorerType > & overApproximation ) {
/*TODO:
template < typename PomdpModelType , typename BeliefValueType >
std : : shared_ptr < RefinementComponents < ValueType > >
ApproximatePOMDPModelchecker < ValueType , RewardModelType > : : computeRefinementStep ( std : : set < uint32_t > const & targetObservations , bool min ,
ApproximatePOMDPModelchecker < PomdpModelType , BeliefValueType > : : computeRefinementStep ( std : : set < uint32_t > const & targetObservations , bool min ,
std : : vector < uint64_t > & observationResolutionVector ,
bool computeRewards ,
std : : shared_ptr < RefinementComponents < ValueType > > refinementComponents ,
@ -504,7 +415,7 @@ namespace storm {
uint64_t nextBeliefId = refinementComponents - > beliefList . size ( ) ;
uint64_t nextStateId = refinementComponents - > overApproxModelPtr - > getNumberOfStates ( ) ;
std : : set < uint64_t > relevantStates ;
std : : set < uint64_t > relevantStates ; // The MDP states where the observation has changed
for ( auto const & iter : refinementComponents - > overApproxBeliefStateMap . left ) {
auto currentBelief = refinementComponents - > beliefList [ iter . first ] ;
if ( changedObservations . find ( currentBelief . observation ) ! = changedObservations . end ( ) ) {
@ -512,7 +423,7 @@ namespace storm {
}
}
std : : set < std : : pair < uint64_t , uint64_t > > statesAndActionsToCheck ;
std : : set < std : : pair < uint64_t , uint64_t > > statesAndActionsToCheck ; // The predecessors of states where the observation has changed
for ( uint64_t state = 0 ; state < refinementComponents - > overApproxModelPtr - > getNumberOfStates ( ) ; + + state ) {
for ( uint_fast64_t row = 0 ; row < refinementComponents - > overApproxModelPtr - > getTransitionMatrix ( ) . getRowGroupSize ( state ) ; + + row ) {
for ( typename storm : : storage : : SparseMatrix < ValueType > : : const_iterator itEntry = refinementComponents - > overApproxModelPtr - > getTransitionMatrix ( ) . getRow (
@ -536,6 +447,7 @@ namespace storm {
action ) ;
std : : map < uint64_t , ValueType > transitionInActionBelief ;
for ( auto iter = actionObservationProbabilities . begin ( ) ; iter ! = actionObservationProbabilities . end ( ) ; + + iter ) {
// Expand and triangulate the successor
uint32_t observation = iter - > first ;
uint64_t idNextBelief = getBeliefAfterActionAndObservation ( refinementComponents - > beliefList , refinementComponents - > beliefIsTarget ,
targetObservations , refinementComponents - > beliefList [ currId ] , action , observation , nextBeliefId ) ;
@ -803,159 +715,11 @@ namespace storm {
refinementComponents - > beliefIsTarget , refinementComponents - > overApproxBeliefStateMap ,
underApproxComponents - > underApproxBeliefStateMap , refinementComponents - > initialBeliefId } ) ;
}
template < typename ValueType , typename RewardModelType >
std : : unique_ptr < POMDPCheckResult < ValueType > >
ApproximatePOMDPModelchecker < ValueType , RewardModelType > : : computeReachabilityRewardOTF ( std : : set < uint32_t > const & targetObservations , bool min ) {
std : : vector < uint64_t > observationResolutionVector ( pomdp . getNrObservations ( ) , options . initialGridResolution ) ;
// return computeReachabilityOTF(targetObservations, min, observationResolutionVector, true);
}
template < typename ValueType , typename RewardModelType >
std : : unique_ptr < POMDPCheckResult < ValueType > >
ApproximatePOMDPModelchecker < ValueType , RewardModelType > : : computeReachabilityProbabilityOTF ( std : : set < uint32_t > const & targetObservations , bool min ) {
std : : vector < uint64_t > observationResolutionVector ( pomdp . getNrObservations ( ) , options . initialGridResolution ) ;
// return computeReachabilityOTF(targetObservations, min, observationResolutionVector, false);
}
template < typename ValueType , typename RewardModelType >
std : : unique_ptr < UnderApproxComponents < ValueType , RewardModelType > >
ApproximatePOMDPModelchecker < ValueType , RewardModelType > : : computeUnderapproximation ( std : : vector < storm : : pomdp : : Belief < ValueType > > & beliefList ,
std : : vector < bool > & beliefIsTarget ,
std : : set < uint32_t > const & targetObservations ,
uint64_t initialBeliefId , bool min ,
bool computeRewards , uint64_t maxModelSize ) {
std : : set < uint64_t > visitedBelieves ;
std : : deque < uint64_t > beliefsToBeExpanded ;
bsmap_type beliefStateMap ;
std : : vector < std : : vector < std : : map < uint64_t , ValueType > > > transitions = { { { { 0 , storm : : utility : : one < ValueType > ( ) } } } ,
{ { { 1 , storm : : utility : : one < ValueType > ( ) } } } } ;
std : : vector < uint64_t > targetStates = { 1 } ;
uint64_t stateId = 2 ;
beliefStateMap . insert ( bsmap_type : : value_type ( initialBeliefId , stateId ) ) ;
+ + stateId ;
uint64_t nextId = beliefList . size ( ) ;
uint64_t counter = 0 ;
statistics . underApproximationBuildTime . start ( ) ;
// Expand the believes
visitedBelieves . insert ( initialBeliefId ) ;
beliefsToBeExpanded . push_back ( initialBeliefId ) ;
while ( ! beliefsToBeExpanded . empty ( ) ) {
//TODO think of other ways to stop exploration besides model size
auto currentBeliefId = beliefsToBeExpanded . front ( ) ;
uint64_t numChoices = pomdp . getNumberOfChoices ( pomdp . getStatesWithObservation ( beliefList [ currentBeliefId ] . observation ) . front ( ) ) ;
// for targets, we only consider one action with one transition
if ( beliefIsTarget [ currentBeliefId ] ) {
// add a self-loop to target states
targetStates . push_back ( beliefStateMap . left . at ( currentBeliefId ) ) ;
transitions . push_back ( { { { beliefStateMap . left . at ( currentBeliefId ) , storm : : utility : : one < ValueType > ( ) } } } ) ;
} else if ( counter > maxModelSize ) {
transitions . push_back ( { { { 0 , storm : : utility : : one < ValueType > ( ) } } } ) ;
} else {
// Iterate over all actions and add the corresponding transitions
std : : vector < std : : map < uint64_t , ValueType > > actionTransitionStorage ;
//TODO add a way to extract the actions from the over-approx and use them here?
for ( uint64_t action = 0 ; action < numChoices ; + + action ) {
std : : map < uint64_t , ValueType > transitionsInStateWithAction ;
std : : map < uint32_t , ValueType > observationProbabilities = computeObservationProbabilitiesAfterAction ( beliefList [ currentBeliefId ] , action ) ;
for ( auto iter = observationProbabilities . begin ( ) ; iter ! = observationProbabilities . end ( ) ; + + iter ) {
uint32_t observation = iter - > first ;
uint64_t nextBeliefId = getBeliefAfterActionAndObservation ( beliefList , beliefIsTarget , targetObservations , beliefList [ currentBeliefId ] ,
action ,
observation , nextId ) ;
nextId = beliefList . size ( ) ;
if ( visitedBelieves . insert ( nextBeliefId ) . second ) {
beliefStateMap . insert ( bsmap_type : : value_type ( nextBeliefId , stateId ) ) ;
+ + stateId ;
beliefsToBeExpanded . push_back ( nextBeliefId ) ;
+ + counter ;
}
transitionsInStateWithAction [ beliefStateMap . left . at ( nextBeliefId ) ] = iter - > second ;
}
actionTransitionStorage . push_back ( transitionsInStateWithAction ) ;
}
transitions . push_back ( actionTransitionStorage ) ;
}
beliefsToBeExpanded . pop_front ( ) ;
if ( storm : : utility : : resources : : isTerminate ( ) ) {
statistics . underApproximationBuildAborted = true ;
break ;
}
}
statistics . underApproximationStates = transitions . size ( ) ;
if ( storm : : utility : : resources : : isTerminate ( ) ) {
statistics . underApproximationBuildTime . stop ( ) ;
return nullptr ;
}
storm : : models : : sparse : : StateLabeling labeling ( transitions . size ( ) ) ;
labeling . addLabel ( " init " ) ;
labeling . addLabel ( " target " ) ;
labeling . addLabelToState ( " init " , 0 ) ;
for ( auto targetState : targetStates ) {
labeling . addLabelToState ( " target " , targetState ) ;
}
std : : shared_ptr < storm : : models : : sparse : : Model < ValueType , RewardModelType > > model ;
auto transitionMatrix = buildTransitionMatrix ( transitions ) ;
if ( transitionMatrix . getRowCount ( ) = = transitionMatrix . getRowGroupCount ( ) ) {
transitionMatrix . makeRowGroupingTrivial ( ) ;
}
storm : : storage : : sparse : : ModelComponents < ValueType , RewardModelType > modelComponents ( transitionMatrix , labeling ) ;
storm : : models : : sparse : : Mdp < ValueType , RewardModelType > underApproxMdp ( modelComponents ) ;
if ( computeRewards ) {
storm : : models : : sparse : : StandardRewardModel < ValueType > rewardModel ( boost : : none , std : : vector < ValueType > ( modelComponents . transitionMatrix . getRowCount ( ) ) ) ;
for ( auto const & iter : beliefStateMap . left ) {
auto currentBelief = beliefList [ iter . first ] ;
auto representativeState = pomdp . getStatesWithObservation ( currentBelief . observation ) . front ( ) ;
for ( uint64_t action = 0 ; action < underApproxMdp . getNumberOfChoices ( iter . second ) ; + + action ) {
// Add the reward
rewardModel . setStateActionReward ( underApproxMdp . getChoiceIndex ( storm : : storage : : StateActionPair ( iter . second , action ) ) ,
getRewardAfterAction ( pomdp . getChoiceIndex ( storm : : storage : : StateActionPair ( representativeState , action ) ) ,
currentBelief ) ) ;
}
}
underApproxMdp . addRewardModel ( " std " , rewardModel ) ;
underApproxMdp . restrictRewardModels ( std : : set < std : : string > ( { " std " } ) ) ;
}
model = std : : make_shared < storm : : models : : sparse : : Mdp < ValueType , RewardModelType > > ( underApproxMdp ) ;
model - > printModelInformationToStream ( std : : cout ) ;
statistics . underApproximationBuildTime . stop ( ) ;
std : : string propertyString ;
if ( computeRewards ) {
propertyString = min ? " Rmin=? [F \" target \" ] " : " Rmax=? [F \" target \" ] " ;
} else {
propertyString = min ? " Pmin=? [F \" target \" ] " : " Pmax=? [F \" target \" ] " ;
}
std : : vector < storm : : jani : : Property > propertyVector = storm : : api : : parseProperties ( propertyString ) ;
std : : shared_ptr < storm : : logic : : Formula const > property = storm : : api : : extractFormulasFromProperties ( propertyVector ) . front ( ) ;
statistics . underApproximationCheckTime . start ( ) ;
std : : unique_ptr < storm : : modelchecker : : CheckResult > res ( storm : : api : : verifyWithSparseEngine < ValueType > ( model , storm : : api : : createTask < ValueType > ( property , false ) ) ) ;
statistics . underApproximationCheckTime . stop ( ) ;
if ( storm : : utility : : resources : : isTerminate ( ) & & ! res ) {
return nullptr ;
}
STORM_LOG_ASSERT ( res , " Result does not exist. " ) ;
res - > filter ( storm : : modelchecker : : ExplicitQualitativeCheckResult ( storm : : storage : : BitVector ( underApproxMdp . getNumberOfStates ( ) , true ) ) ) ;
auto underApproxResultMap = res - > asExplicitQuantitativeCheckResult < ValueType > ( ) . getValueMap ( ) ;
auto underApprox = underApproxResultMap [ beliefStateMap . left . at ( initialBeliefId ) ] ;
return std : : make_unique < UnderApproxComponents < ValueType > > ( UnderApproxComponents < ValueType > { underApprox , underApproxResultMap , beliefStateMap } ) ;
*/
}
template < typename ValueType , typename RewardModelType >
std : : unique_ptr < UnderApproxComponents < ValueType , RewardModelType > >
ApproximatePOMDPModelchecker < ValueType , RewardModelType > : : computeUnderapproximation ( std : : shared_ptr < storm : : storage : : BeliefManager < storm : : models : : sparse : : Pomdp < ValueType > > > beliefManager ,
std : : set < uint32_t > const & targetObservations , bool min ,
bool computeRewards , uint64_t maxModelSize , std : : vector < ValueType > const & lowerPomdpValueBounds , std : : vector < ValueType > const & upperPomdpValueBounds ) {
// Build the belief MDP until enough states are explored.
//TODO think of other ways to stop exploration besides model size
template < typename PomdpModelType , typename BeliefValueType >
std : : shared_ptr < typename ApproximatePOMDPModelchecker < PomdpModelType , BeliefValueType > : : ExplorerType > ApproximatePOMDPModelchecker < PomdpModelType , BeliefValueType > : : computeUnderApproximation ( std : : set < uint32_t > const & targetObservations , bool min , bool computeRewards , std : : vector < ValueType > const & lowerPomdpValueBounds , std : : vector < ValueType > const & upperPomdpValueBounds , uint64_t maxStateCount , std : : shared_ptr < BeliefManagerType > & beliefManager ) {
statistics . underApproximationBuildTime . start ( ) ;
storm : : builder : : BeliefMdpExplorer < storm : : models : : sparse : : Pomdp < ValueType > > explorer ( beliefManager , lowerPomdpValueBounds , upperPomdpValueBounds ) ;
@ -981,7 +745,7 @@ namespace storm {
if ( storm : : utility : : abs < ValueType > ( explorer . getUpperValueBoundAtCurrentState ( ) - explorer . getLowerValueBoundAtCurrentState ( ) ) < options . explorationThreshold ) {
stopExploration = true ;
explorer . setCurrentStateIsTruncated ( ) ;
} else if ( explorer . getCurrentNumberOfMdpStates ( ) > = maxModelSize ) {
} else if ( explorer . getCurrentNumberOfMdpStates ( ) > = maxStateCount ) {
stopExploration = true ;
explorer . setCurrentStateIsTruncated ( ) ;
}
@ -1024,249 +788,22 @@ namespace storm {
explorer . finishExploration ( ) ;
statistics . underApproximationBuildTime . stop ( ) ;
STORM_PRINT ( " Under Approximation MDP build took " < < statistics . underApproximationBuildTime < < " seconds. " < < std : : endl ) ;
explorer . getExploredMdp ( ) - > printModelInformationToStream ( std : : cout ) ;
statistics . underApproximationCheckTime . start ( ) ;
explorer . computeValuesOfExploredMdp ( min ? storm : : solver : : OptimizationDirection : : Minimize : storm : : solver : : OptimizationDirection : : Maximize ) ;
statistics . underApproximationCheckTime . stop ( ) ;
STORM_PRINT ( " Time Underapproximation: " < < statistics . underApproximationCheckTime < < " seconds. " < < std : : endl ) ;
STORM_PRINT ( " Under-Approximation Result: " < < explorer . getComputedValueAtInitialState ( ) < < std : : endl ) ;
return std : : make_unique < UnderApproxComponents < ValueType > > ( UnderApproxComponents < ValueType > { explorer . getComputedValueAtInitialState ( ) , { } , { } } ) ;
return std : : make_shared < ExplorerType > ( std : : move ( explorer ) ) ;
}
template < typename ValueType , typename RewardModelType >
storm : : storage : : SparseMatrix < ValueType >
ApproximatePOMDPModelchecker < ValueType , RewardModelType > : : buildTransitionMatrix ( std : : vector < std : : vector < std : : map < uint64_t , ValueType > > > & transitions ) {
uint_fast64_t currentRow = 0 ;
uint_fast64_t currentRowGroup = 0 ;
uint64_t nrColumns = transitions . size ( ) ;
uint64_t nrRows = 0 ;
uint64_t nrEntries = 0 ;
for ( auto const & actionTransitions : transitions ) {
for ( auto const & map : actionTransitions ) {
nrEntries + = map . size ( ) ;
+ + nrRows ;
}
}
storm : : storage : : SparseMatrixBuilder < ValueType > smb ( nrRows , nrColumns , nrEntries , true , true ) ;
for ( auto const & actionTransitions : transitions ) {
smb . newRowGroup ( currentRow ) ;
for ( auto const & map : actionTransitions ) {
for ( auto const & transition : map ) {
smb . addNextValue ( currentRow , transition . first , transition . second ) ;
}
+ + currentRow ;
}
+ + currentRowGroup ;
}
return smb . build ( ) ;
}
template < typename ValueType , typename RewardModelType >
uint64_t ApproximatePOMDPModelchecker < ValueType , RewardModelType > : : getBeliefIdInVector (
std : : vector < storm : : pomdp : : Belief < ValueType > > const & grid , uint32_t observation ,
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 ( 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 ;
}
}
if ( same ) {
return belief . id ;
}
}
}
return - 1 ;
}
template < typename ValueType , typename RewardModelType >
storm : : pomdp : : Belief < ValueType > ApproximatePOMDPModelchecker < ValueType , RewardModelType > : : getInitialBelief ( uint64_t id ) {
STORM_LOG_ASSERT ( pomdp . getInitialStates ( ) . getNumberOfSetBits ( ) < 2 ,
" POMDP contains more than one initial state " ) ;
STORM_LOG_ASSERT ( pomdp . getInitialStates ( ) . getNumberOfSetBits ( ) = = 1 ,
" POMDP does not contain an initial state " ) ;
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 ;
}
template < typename PomdpModelType , typename BeliefValueType >
void ApproximatePOMDPModelchecker < PomdpModelType , BeliefValueType > : : refineUnderApproximation ( std : : set < uint32_t > const & targetObservations , bool min , bool computeRewards , uint64_t maxStateCount , std : : shared_ptr < BeliefManagerType > & beliefManager , std : : shared_ptr < ExplorerType > & underApproximation ) {
// TODO
}
return storm : : pomdp : : Belief < ValueType > { id , observation , distribution } ;
}
template < typename ValueType , typename RewardModelType >
std : : pair < std : : vector < std : : map < uint64_t , ValueType > > , std : : vector < ValueType > >
ApproximatePOMDPModelchecker < ValueType , RewardModelType > : : computeSubSimplexAndLambdas (
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
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 < 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 ] ;
}
auto p = storm : : utility : : vector : : getSortedIndices ( d ) ;
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 < nrStates ; + + j ) {
qs [ i ] [ j ] = v [ j ] ;
}
} else {
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 {
qs [ i ] [ j ] = qs [ i - 1 ] [ j ] ;
}
}
}
}
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 ;
}
}
if ( cc . isLess ( storm : : utility : : zero < ValueType > ( ) , qs [ j ] [ nrStates - 1 ] ) ) {
subSimplex [ j ] [ nrStates - 1 ] = qs [ j ] [ nrStates - 1 ] / convResolution ;
}
}
std : : vector < ValueType > lambdas ( nrStates , storm : : utility : : zero < ValueType > ( ) ) ;
auto sum = storm : : utility : : zero < ValueType > ( ) ;
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 ] ] ;
}
lambdas [ 0 ] = storm : : utility : : one < ValueType > ( ) - sum ;
return std : : make_pair ( subSimplex , lambdas ) ;
}
template < typename ValueType , typename RewardModelType >
std : : map < uint32_t , ValueType >
ApproximatePOMDPModelchecker < ValueType , RewardModelType > : : computeObservationProbabilitiesAfterAction (
storm : : pomdp : : Belief < ValueType > & belief ,
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 : : map < uint64_t , ValueType > postProbabilities ;
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 ) {
postProbabilities [ entry . getColumn ( ) ] + = belief . probabilities [ state ] * entry . getValue ( ) ;
}
}
}
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 ;
}
template < typename ValueType , typename RewardModelType >
uint64_t ApproximatePOMDPModelchecker < ValueType , RewardModelType > : : getBeliefAfterActionAndObservation ( std : : vector < storm : : pomdp : : Belief < ValueType > > & beliefList ,
std : : vector < bool > & beliefIsTarget , std : : set < uint32_t > const & targetObservations , storm : : pomdp : : Belief < ValueType > & belief , uint64_t actionIndex ,
uint32_t observation , uint64_t id ) {
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 ( ) ;
}
}
}
// We have to normalize the distribution
auto sum = storm : : utility : : zero < ValueType > ( ) ;
for ( auto const & entry : distributionAfter ) {
sum + = entry . second ;
}
for ( auto const & entry : distributionAfter ) {
distributionAfter [ entry . first ] / = sum ;
}
if ( getBeliefIdInVector ( beliefList , observation , distributionAfter ) ! = uint64_t ( - 1 ) ) {
auto res = getBeliefIdInVector ( beliefList , observation , distributionAfter ) ;
return res ;
} else {
beliefList . push_back ( storm : : pomdp : : Belief < ValueType > { id , observation , distributionAfter } ) ;
beliefIsTarget . push_back ( targetObservations . find ( observation ) ! = targetObservations . end ( ) ) ;
return id ;
}
}
template < typename ValueType , typename RewardModelType >
ValueType ApproximatePOMDPModelchecker < ValueType , RewardModelType > : : getRewardAfterAction ( uint64_t action , std : : map < uint64_t , ValueType > const & belief ) {
auto result = storm : : utility : : zero < ValueType > ( ) ;
for ( auto const & probEntry : belief ) {
result + = probEntry . second * pomdp . getUniqueRewardModel ( ) . getTotalStateActionReward ( probEntry . first , action , pomdp . getTransitionMatrix ( ) ) ;
}
return result ;
}
template < typename ValueType , typename RewardModelType >
ValueType ApproximatePOMDPModelchecker < ValueType , RewardModelType > : : getRewardAfterAction ( uint64_t action , storm : : pomdp : : Belief < ValueType > const & belief ) {
auto result = storm : : utility : : zero < ValueType > ( ) ;
for ( auto const & probEntry : belief . probabilities ) {
result + = probEntry . second * pomdp . getUniqueRewardModel ( ) . getTotalStateActionReward ( probEntry . first , action , pomdp . getTransitionMatrix ( ) ) ;
}
return result ;
}
template
class ApproximatePOMDPModelchecker < double > ;
# ifdef STORM_HAVE_CARL
template
class ApproximatePOMDPModelchecker < storm : : RationalNumber > ;
template class ApproximatePOMDPModelchecker < storm : : models : : sparse : : Pomdp < double > > ;
template class ApproximatePOMDPModelchecker < storm : : models : : sparse : : Pomdp < storm : : RationalNumber > > ;
# endif
}
}
}
Tim Quatmann
5 years ago