@ -5,12 +5,13 @@
# include "storm/utility/ConstantsComparator.h"
# include "storm/models/sparse/Dtmc.h"
# 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/models/sparse/StandardRewardModel.h "
# include "storm/modelchecker/hints/ExplicitModelCheckerHint.cpp "
# include "storm/api/properties.h"
# include "storm/api/export.h"
# include "storm-parsers/api/storm-parsers.h"
@ -22,8 +23,9 @@ namespace storm {
ApproximatePOMDPModelchecker < ValueType , RewardModelType > : : ApproximatePOMDPModelchecker ( ) {
precision = 0.000000001 ;
cc = storm : : utility : : ConstantsComparator < ValueType > ( storm : : utility : : convertNumber < ValueType > ( precision ) , false ) ;
useMdp = fals e;
useMdp = tru e;
maxIterations = 1000 ;
cacheSubsimplices = false ;
}
template < typename ValueType , typename RewardModelType >
@ -51,7 +53,56 @@ namespace storm {
std : : unique_ptr < POMDPCheckResult < ValueType > >
ApproximatePOMDPModelchecker < ValueType , RewardModelType > : : computeReachabilityOTF ( storm : : models : : sparse : : Pomdp < ValueType , RewardModelType > const & pomdp ,
std : : set < uint32_t > const & targetObservations , bool min , uint64_t gridResolution ,
bool computeRewards ) {
bool computeRewards , double explorationThreshold ) {
//TODO For the prototypical implementation, I put the refinement loop here. I'll change this later on
storm : : utility : : Stopwatch underlyingWatch ( true ) ;
storm : : models : : sparse : : StateLabeling underlyingMdpLabeling ( pomdp . getStateLabeling ( ) ) ;
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 )
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 mdpResultMap = underlyingRes - > asExplicitQuantitativeCheckResult < ValueType > ( ) . getValueMap ( ) ;
underlyingWatch . stop ( ) ;
storm : : utility : : Stopwatch positionalWatch ( true ) ;
// we define some positional scheduler for the POMDP as an experimental 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 ) ;
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 mdpUnderapproxResultMap = underapproxRes - > asExplicitQuantitativeCheckResult < ValueType > ( ) . getValueMap ( ) ;
positionalWatch . stop ( ) ;
STORM_PRINT ( " Preprocessing Times: " < < underlyingWatch < < " / " < < positionalWatch < < std : : endl )
STORM_PRINT ( " Use On-The-Fly Grid Generation " < < std : : endl )
std : : vector < storm : : pomdp : : Belief < ValueType > > beliefList ;
std : : vector < bool > beliefIsTarget ;
@ -70,46 +121,74 @@ namespace storm {
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 ;
storm : : utility : : Stopwatch expansionTimer ( true ) ;
// Initial belief always has ID 0
// Initial belief always has belief ID 0
storm : : pomdp : : Belief < ValueType > initialBelief = getInitialBelief ( pomdp , nextId ) ;
+ + nextId ;
beliefList . push_back ( initialBelief ) ;
beliefIsTarget . push_back ( targetObservations . find ( initialBelief . observation ) ! = targetObservations . end ( ) ) ;
// These are the components to build the MDP from the grid
// These are the components to build the MDP from the grid TODO make a better stucture to allow for fast reverse lookups (state-->belief) as it is a bijective function (boost:bimap?)
std : : map < uint64_t , uint64_t > beliefStateMap ;
std : : vector < std : : vector < std : : map < uint64_t , ValueType > > > mdpTransitions ;
std : : vector < uint64_t > targetStates ;
uint64_t mdpStateId = 0 ;
// Reserve states 0 and 1 as always sink/goal states
std : : vector < std : : vector < std : : map < uint64_t , ValueType > > > mdpTransitions = { { { { 0 , storm : : utility : : one < ValueType > ( ) } } } , { { { 1 , storm : : utility : : one < ValueType > ( ) } } } } ;
// Hint vector for the MDP modelchecker (initialize with constant sink/goal values)
std : : vector < ValueType > hintVector = { storm : : utility : : zero < ValueType > ( ) , storm : : utility : : one < ValueType > ( ) } ;
std : : vector < uint64_t > targetStates = { 1 } ;
uint64_t mdpStateId = 2 ;
beliefStateMap [ initialBelief . id ] = mdpStateId ;
+ + mdpStateId ;
// Map to save the weighted values resulting from the preprocessing for the beliefs / indices in beliefSpace
std : : map < uint64_t , ValueType > weightedSumOverMap ;
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 , gridResolution ) ;
std : : vector < std : : vector < ValueType > > initSubSimplex = initTemp . first ;
std : : vector < ValueType > initLambdas = initTemp . second ;
subSimplexCache [ 0 ] = initSubSimplex ;
lambda Cache[ 0 ] = initLambdas ;
bool initInserted = false ;
if ( cacheSubsimplices ) {
subSimplex Cache [ 0 ] = initSubSimplex ;
lambdaCache [ 0 ] = initLambdas ;
}
std : : vector < std : : map < uint64_t , ValueType > > initTransitionsInBelief ;
std : : map < uint64_t , ValueType > initTransitionInActionBelief ;
bool initInserted = false ;
for ( size_t j = 0 ; j < initLambdas . size ( ) ; + + j ) {
if ( ! cc . isEqual ( initLambdas [ j ] , storm : : utility : : zero < ValueType > ( ) ) ) {
uint64_t searchResult = getBeliefIdInVector ( beliefList , initialBelief . observation , initSubSimplex [ j ] ) ;
if ( searchResult = = uint64_t ( - 1 ) | | ( searchResult = = 0 & & ! initInserted ) ) {
if ( searchResult = = 0 ) {
// the initial belief is on the grid itself
auto tempWeightedSumOver = storm : : utility : : zero < ValueType > ( ) ;
auto tempWeightedSumUnder = storm : : utility : : zero < ValueType > ( ) ;
for ( uint64_t i = 0 ; i < initSubSimplex [ j ] . size ( ) ; + + i ) {
tempWeightedSumOver + = initSubSimplex [ j ] [ i ] * storm : : utility : : convertNumber < ValueType > ( mdpResultMap [ i ] ) ;
tempWeightedSumUnder + = initSubSimplex [ j ] [ i ] * storm : : utility : : convertNumber < ValueType > ( mdpUnderapproxResultMap [ i ] ) ;
}
weightedSumOverMap [ initialBelief . id ] = tempWeightedSumOver ;
weightedSumUnderMap [ initialBelief . id ] = tempWeightedSumUnder ;
initInserted = true ;
beliefGrid . push_back ( initialBelief ) ;
beliefsToBeExpanded . push_back ( 0 ) ;
hintVector . push_back ( targetObservations . find ( initialBelief . observation ) ! = targetObservations . end ( ) ? storm : : utility : : one < ValueType > ( )
: storm : : utility : : zero < ValueType > ( ) ) ;
} else {
// if the triangulated belief was not found in the list, we place it in the grid and add it to the work list
auto tempWeightedSumOver = storm : : utility : : zero < ValueType > ( ) ;
auto tempWeightedSumUnder = storm : : utility : : zero < ValueType > ( ) ;
for ( uint64_t i = 0 ; i < initSubSimplex [ j ] . size ( ) ; + + i ) {
tempWeightedSumOver + = initSubSimplex [ j ] [ i ] * storm : : utility : : convertNumber < ValueType > ( mdpResultMap [ i ] ) ;
tempWeightedSumUnder + = initSubSimplex [ j ] [ i ] * storm : : utility : : convertNumber < ValueType > ( mdpUnderapproxResultMap [ i ] ) ;
}
weightedSumOverMap [ nextId ] = tempWeightedSumOver ;
weightedSumUnderMap [ nextId ] = tempWeightedSumUnder ;
storm : : pomdp : : Belief < ValueType > gridBelief = { nextId , initialBelief . observation , initSubSimplex [ j ] } ;
beliefList . push_back ( gridBelief ) ;
beliefGrid . push_back ( gridBelief ) ;
@ -117,6 +196,9 @@ namespace storm {
beliefsToBeExpanded . push_back ( nextId ) ;
+ + nextId ;
hintVector . push_back ( targetObservations . find ( initialBelief . observation ) ! = targetObservations . end ( ) ? storm : : utility : : one < ValueType > ( )
: storm : : utility : : zero < ValueType > ( ) ) ;
beliefStateMap [ nextId ] = mdpStateId ;
initTransitionInActionBelief [ mdpStateId ] = initLambdas [ j ] ;
+ + nextId ;
@ -132,15 +214,24 @@ namespace storm {
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
//beliefsToBeExpanded.push_back(initialBelief.id); TODO 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 to avoid unreachable grid points
// Expand the beliefs to generate the grid on-the-fly
if ( explorationThreshold > 0 ) {
STORM_PRINT ( " Exploration threshold: " < < explorationThreshold < < std : : endl )
}
while ( ! beliefsToBeExpanded . empty ( ) ) {
// TODO add direct generation of transition matrix
uint64_t currId = beliefsToBeExpanded . front ( ) ;
beliefsToBeExpanded . pop_front ( ) ;
bool isTarget = beliefIsTarget [ currId ] ;
if ( cc . isLess ( weightedSumOverMap [ currId ] - weightedSumUnderMap [ currId ] , storm : : utility : : convertNumber < ValueType > ( explorationThreshold ) ) ) {
result . emplace ( std : : make_pair ( currId , computeRewards ? storm : : utility : : zero < ValueType > ( ) : weightedSumOverMap [ currId ] ) ) ;
mdpTransitions . push_back ( { { { 1 , weightedSumOverMap [ currId ] } , { 0 , storm : : utility : : one < ValueType > ( ) - weightedSumOverMap [ currId ] } } } ) ;
continue ;
}
if ( isTarget ) {
// Depending on whether we compute rewards, we select the right initial result
result . emplace ( std : : make_pair ( currId , computeRewards ? storm : : utility : : zero < ValueType > ( ) : storm : : utility : : one < ValueType > ( ) ) ) ;
@ -177,17 +268,17 @@ namespace storm {
//Triangulate here and put the possibly resulting belief in the grid
std : : vector < std : : vector < ValueType > > subSimplex ;
std : : vector < ValueType > lambdas ;
if ( subSimplexCache . count ( idNextBelief ) > 0 ) {
// TODO is this necessary here? Think later
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 , gridResolution ) ;
std : : pair < std : : vector < std : : vector < ValueType > > , std : : vector < ValueType > > temp = computeSubSimplexAndLambdas ( beliefList [ idNextBelief ] . probabilities , gridResolution ) ;
subSimplex = temp . first ;
lambdas = temp . second ;
subSimplexCache [ idNextBelief ] = subSimplex ;
lambdaCache [ idNextBelief ] = lambdas ;
if ( cacheSubsimplices ) {
subSimplexCache [ idNextBelief ] = subSimplex ;
lambdaCache [ idNextBelief ] = lambdas ;
}
}
for ( size_t j = 0 ; j < lambdas . size ( ) ; + + j ) {
@ -197,9 +288,24 @@ namespace storm {
storm : : pomdp : : Belief < ValueType > gridBelief = { nextId , observation , subSimplex [ j ] } ;
beliefList . push_back ( gridBelief ) ;
beliefGrid . push_back ( gridBelief ) ;
beliefIsTarget . push_back (
targetObservations . find ( observation ) ! = targetObservations . end ( ) ) ;
// compute overapproximate value using MDP result map
auto tempWeightedSumOver = storm : : utility : : zero < ValueType > ( ) ;
auto tempWeightedSumUnder = storm : : utility : : zero < ValueType > ( ) ;
for ( uint64_t i = 0 ; i < subSimplex [ j ] . size ( ) ; + + i ) {
tempWeightedSumOver + = subSimplex [ j ] [ i ] * storm : : utility : : convertNumber < ValueType > ( mdpResultMap [ i ] ) ;
tempWeightedSumUnder + = subSimplex [ j ] [ i ] * storm : : utility : : convertNumber < ValueType > ( mdpUnderapproxResultMap [ i ] ) ;
}
beliefIsTarget . push_back ( targetObservations . find ( observation ) ! = targetObservations . end ( ) ) ;
if ( cc . isEqual ( tempWeightedSumOver , tempWeightedSumUnder ) ) {
hintVector . push_back ( tempWeightedSumOver ) ;
} else {
hintVector . push_back ( storm : : utility : : zero < ValueType > ( ) ) ;
}
beliefsToBeExpanded . push_back ( nextId ) ;
weightedSumOverMap [ nextId ] = tempWeightedSumOver ;
weightedSumUnderMap [ nextId ] = tempWeightedSumUnder ;
beliefStateMap [ nextId ] = mdpStateId ;
transitionInActionBelief [ mdpStateId ] = iter - > second * lambdas [ j ] ;
@ -241,14 +347,15 @@ namespace storm {
STORM_PRINT ( " #Believes in List: " < < beliefList . size ( ) < < std : : endl )
STORM_PRINT ( " Belief space expansion took " < < expansionTimer < < std : : endl )
//auto overApprox = overApproximationValueIteration(pomdp, beliefList, beliefGrid, beliefIsTarget, observationProbabilities, nextBelieves, beliefActionRewards, subSimplexCache, lambdaCache,result, chosenActions, gridResolution, min, computeRewards);
storm : : models : : sparse : : StateLabeling mdpLabeling ( mdpTransitions . size ( ) ) ;
mdpLabeling . addLabel ( " init " ) ;
mdpLabeling . addLabel ( " target " ) ;
mdpLabeling . addLabelToState ( " init " , 0 ) ;
mdpLabeling . addLabelToState ( " init " , beliefStateMap [ initialBelief . id ] ) ;
for ( auto targetState : targetStates ) {
mdpLabeling . addLabelToState ( " target " , targetState ) ;
}
storm : : storage : : sparse : : ModelComponents < ValueType , RewardModelType > modelComponents ( buildTransitionMatrix ( mdpTransitions ) , mdpLabeling ) ;
storm : : models : : sparse : : Mdp < ValueType , RewardModelType > overApproxMdp ( modelComponents ) ;
if ( computeRewards ) {
@ -268,26 +375,6 @@ namespace storm {
}
overApproxMdp . printModelInformationToStream ( std : : cout ) ;
storm : : utility : : Stopwatch overApproxTimer ( true ) ;
auto overApprox = overApproximationValueIteration ( pomdp , beliefList , beliefGrid , beliefIsTarget , observationProbabilities , nextBelieves , beliefActionRewards ,
subSimplexCache , lambdaCache ,
result , chosenActions , gridResolution , min , computeRewards ) ;
overApproxTimer . stop ( ) ;
auto underApprox = storm : : utility : : zero < ValueType > ( ) ;
/*
storm : : utility : : Stopwatch underApproxTimer ( true ) ;
ValueType underApprox = computeUnderapproximationWithMDP ( pomdp , beliefList , beliefIsTarget , targetObservations , observationProbabilities , nextBelieves ,
result , chosenActions , gridResolution , initialBelief . id , min , computeRewards ) ;
underApproxTimer . stop ( ) ;
STORM_PRINT ( " Time Overapproximation: " < < overApproxTimer
< < std : : endl
< < " Time Underapproximation: " < < underApproxTimer
< < std : : endl ) ; */
STORM_PRINT ( " Over-Approximation Result: " < < overApprox < < std : : endl ) ;
//STORM_PRINT("Under-Approximation Result: " << underApprox << std::endl);
auto model = std : : make_shared < storm : : models : : sparse : : Mdp < ValueType , RewardModelType > > ( overApproxMdp ) ;
auto modelPtr = std : : static_pointer_cast < storm : : models : : sparse : : Model < ValueType , RewardModelType > > ( model ) ;
std : : vector < std : : string > parameterNames ;
@ -298,11 +385,32 @@ namespace storm {
propertyString + = " =? [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 ( ) ;
std : : unique_ptr < storm : : modelchecker : : CheckResult > res ( storm : : api : : verifyWithSparseEngine < ValueType > ( model , storm : : api : : createTask < ValueType > ( property , true ) ) ) ;
auto task = storm : : api : : createTask < ValueType > ( property , false ) ;
auto hint = storm : : modelchecker : : ExplicitModelCheckerHint < ValueType > ( ) ;
hint . setResultHint ( hintVector ) ;
auto hintPtr = std : : make_shared < storm : : modelchecker : : ExplicitModelCheckerHint < ValueType > > ( hint ) ;
task . setHint ( hintPtr ) ;
storm : : utility : : Stopwatch overApproxTimer ( true ) ;
std : : unique_ptr < storm : : modelchecker : : CheckResult > res ( storm : : api : : verifyWithSparseEngine < ValueType > ( model , task ) ) ;
overApproxTimer . stop ( ) ;
STORM_LOG_ASSERT ( res , " Result not exist. " ) ;
res - > filter ( storm : : modelchecker : : ExplicitQualitativeCheckResult ( model - > getInitialStates ( ) ) ) ;
STORM_PRINT ( " OverApprox MDP: " < < ( res - > asExplicitQuantitativeCheckResult < ValueType > ( ) . getValueMap ( ) . begin ( ) - > second ) < < std : : endl ) ;
res - > filter ( storm : : modelchecker : : ExplicitQualitativeCheckResult ( storm : : storage : : BitVector ( overApproxMdp . getNumberOfStates ( ) , true ) ) ) ;
auto resultMap = res - > asExplicitQuantitativeCheckResult < ValueType > ( ) . getValueMap ( ) ;
auto overApprox = resultMap [ beliefStateMap [ initialBelief . id ] ] ;
/* storm::utility::Stopwatch underApproxTimer(true);
ValueType underApprox = computeUnderapproximationWithMDP ( pomdp , beliefList , beliefIsTarget , targetObservations , observationProbabilities , nextBelieves ,
result , chosenActions , gridResolution , initialBelief . id , min , computeRewards ) ;
underApproxTimer . stop ( ) ; */
STORM_PRINT ( " Time Overapproximation: " < < overApproxTimer < < std : : endl )
auto underApprox = storm : : utility : : zero < ValueType > ( ) ;
STORM_PRINT ( " Over-Approximation Result: " < < overApprox < < std : : endl ) ;
STORM_PRINT ( " Under-Approximation Result: " < < underApprox < < std : : endl ) ;
std : : map < uint64_t , ValueType > differences ;
for ( auto const & entry : weightedSumUnderMap ) {
differences [ beliefStateMap [ entry . first ] ] = resultMap [ beliefStateMap [ entry . first ] ] - weightedSumUnderMap [ entry . first ] ;
}
return std : : make_unique < POMDPCheckResult < ValueType > > ( POMDPCheckResult < ValueType > { overApprox , underApprox } ) ;
}
@ -350,7 +458,7 @@ namespace storm {
// cache the values to not always re-calculate
std : : vector < std : : vector < ValueType > > subSimplex ;
std : : vector < ValueType > lambdas ;
if ( subSimplexCache . count ( nextBelief . id ) > 0 ) {
if ( cacheSubsimplices & & subSimplexCache . count ( nextBelief . id ) > 0 ) {
subSimplex = subSimplexCache [ nextBelief . id ] ;
lambdas = lambdaCache [ nextBelief . id ] ;
} else {
@ -358,8 +466,10 @@ namespace storm {
gridResolution ) ;
subSimplex = temp . first ;
lambdas = temp . second ;
subSimplexCache [ nextBelief . id ] = subSimplex ;
lambdaCache [ nextBelief . id ] = lambdas ;
if ( cacheSubsimplices ) {
subSimplexCache [ nextBelief . id ] = subSimplex ;
lambdaCache [ nextBelief . id ] = lambdas ;
}
}
auto sum = storm : : utility : : zero < ValueType > ( ) ;
for ( size_t j = 0 ; j < lambdas . size ( ) ; + + j ) {
@ -402,11 +512,21 @@ namespace storm {
STORM_PRINT ( " Overapproximation took " < < iteration < < " iterations " < < std : : endl ) ;
std : : vector < ValueType > initialLambda ;
std : : vector < std : : vector < ValueType > > initialSubsimplex ;
if ( cacheSubsimplices ) {
initialLambda = lambdaCache [ 0 ] ;
initialSubsimplex = subSimplexCache [ 0 ] ;
} else {
auto temp = computeSubSimplexAndLambdas ( beliefList [ 0 ] . probabilities , gridResolution ) ;
initialSubsimplex = temp . first ;
initialLambda = temp . second ;
}
auto overApprox = storm : : utility : : zero < ValueType > ( ) ;
for ( size_t j = 0 ; j < lambdaCache [ 0 ] . size ( ) ; + + j ) {
if ( lambdaCache [ 0 ] [ j ] ! = storm : : utility : : zero < ValueType > ( ) ) {
overApprox + = lambdaCache [ 0 ] [ j ] * result_backup [ getBeliefIdInVector ( beliefGrid , beliefList [ 0 ] . observation , subSimplexCache [ 0 ] [ j ] ) ] ;
for ( size_t j = 0 ; j < initia lL ambda. size ( ) ; + + j ) {
if ( initia lL ambda[ j ] ! = storm : : utility : : zero < ValueType > ( ) ) {
overApprox + = initia lL ambda[ j ] * result_backup [ getBeliefIdInVector ( beliefGrid , beliefList [ 0 ] . observation , initialSubsimplex [ j ] ) ] ;
}
}
return overApprox ;
@ -417,15 +537,15 @@ namespace storm {
ApproximatePOMDPModelchecker < ValueType , RewardModelType > : : computeReachabilityRewardOTF ( storm : : models : : sparse : : Pomdp < ValueType , RewardModelType > const & pomdp ,
std : : set < uint32_t > const & targetObservations , bool min ,
uint64_t gridResolution ) {
return computeReachabilityOTF ( pomdp , targetObservations , min , gridResolution , true ) ;
return computeReachabilityOTF ( pomdp , targetObservations , min , gridResolution , true , 0 ) ;
}
template < typename ValueType , typename RewardModelType >
std : : unique_ptr < POMDPCheckResult < ValueType > >
ApproximatePOMDPModelchecker < ValueType , RewardModelType > : : computeReachabilityProbabilityOTF ( storm : : models : : sparse : : Pomdp < ValueType , RewardModelType > const & pomdp ,
std : : set < uint32_t > const & targetObservations , bool min ,
uint64_t gridResolution ) {
return computeReachabilityOTF ( pomdp , targetObservations , min , gridResolution , false ) ;
uint64_t gridResolution , double explorationThreshold ) {
return computeReachabilityOTF ( pomdp , targetObservations , min , gridResolution , false , explorationThreshold ) ;
}
template < typename ValueType , typename RewardModelType >
@ -466,8 +586,10 @@ namespace storm {
std : : map < uint64_t , std : : vector < ValueType > > lambdaCache ;
std : : pair < std : : vector < std : : vector < ValueType > > , std : : vector < ValueType > > temp = computeSubSimplexAndLambdas ( initialBelief . probabilities , gridResolution ) ;
subSimplexCache [ 0 ] = temp . first ;
lambdaCache [ 0 ] = temp . second ;
if ( cacheSubsimplices ) {
subSimplexCache [ 0 ] = temp . first ;
lambdaCache [ 0 ] = temp . second ;
}
storm : : utility : : Stopwatch nextBeliefGeneration ( true ) ;
for ( size_t i = 0 ; i < beliefGrid . size ( ) ; + + i ) {
@ -478,6 +600,7 @@ namespace storm {
} else {
result . emplace ( std : : make_pair ( currentBelief . id , storm : : utility : : zero < ValueType > ( ) ) ) ;
//TODO put this in extra function
// As we need to grab some parameters which are the same for all states with the same observation, we simply select some state as the representative
uint64_t representativeState = pomdp . getStatesWithObservation ( currentBelief . observation ) . front ( ) ;
uint64_t numChoices = pomdp . getNumberOfChoices ( representativeState ) ;
@ -563,7 +686,7 @@ 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 , ValueType > & result ,
std : : map < uint64_t , std : : vector < uint64_t > > chosenActions ,
std : : map < uint64_t , std : : vector < uint64_t > > & chosenActions ,
uint64_t gridResolution , uint64_t initialBeliefId , bool min ,
bool computeRewards , bool generateMdp ) {
std : : set < uint64_t > visitedBelieves ;
@ -691,8 +814,7 @@ namespace storm {
template < typename ValueType , typename RewardModelType >
storm : : storage : : SparseMatrix < ValueType >
ApproximatePOMDPModelchecker < ValueType , RewardModelType > : : buildTransitionMatrix (
std : : vector < std : : vector < std : : map < uint64_t , ValueType > > > transitions ) {
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 ( ) ;
@ -811,7 +933,7 @@ 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 : : vector < ValueType > & probabilities ) {
// TODO This one is quite slow
for ( auto const & belief : grid ) {
if ( belief . observation = = observation ) {
@ -931,7 +1053,7 @@ namespace storm {
template < typename ValueType , typename RewardModelType >
std : : pair < std : : vector < std : : vector < ValueType > > , std : : vector < ValueType > >
ApproximatePOMDPModelchecker < ValueType , RewardModelType > : : computeSubSimplexAndLambdas (
std : : vector < ValueType > probabilities , uint64_t resolution ) {
std : : vector < ValueType > & probabilities , uint64_t resolution ) {
// 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 ( ) ;
@ -991,7 +1113,7 @@ namespace storm {
std : : map < uint32_t , ValueType >
ApproximatePOMDPModelchecker < ValueType , RewardModelType > : : computeObservationProbabilitiesAfterAction (
storm : : models : : sparse : : Pomdp < ValueType , RewardModelType > const & pomdp ,
storm : : pomdp : : Belief < ValueType > belief ,
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)
@ -1011,10 +1133,8 @@ namespace storm {
template < typename ValueType , typename RewardModelType >
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 ) {
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 > ( ) ) ;
uint32_t observation = 0 ;
for ( uint64_t state = 0 ; state < pomdp . getNumberOfStates ( ) ; + + state ) {
@ -1032,9 +1152,8 @@ namespace storm {
template < typename ValueType , typename RewardModelType >
uint64_t ApproximatePOMDPModelchecker < ValueType , RewardModelType > : : getBeliefAfterActionAndObservation (
storm : : models : : sparse : : Pomdp < ValueType , RewardModelType > const & pomdp , std : : vector < storm : : pomdp : : Belief < ValueType > > & beliefList ,
std : : vector < bool > & beliefIsTarget , std : : set < uint32_t > const & targetObservations , storm : : pomdp : : Belief < ValueType > belief , uint64_t actionIndex ,
uint32_t observation ,
uint64_t id ) {
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 ) {
@ -1077,7 +1196,7 @@ namespace storm {
template < typename ValueType , typename RewardModelType >
ValueType ApproximatePOMDPModelchecker < ValueType , RewardModelType > : : getRewardAfterAction ( storm : : models : : sparse : : Pomdp < ValueType , RewardModelType > const & pomdp ,
uint64_t action , storm : : pomdp : : Belief < ValueType > belief ) {
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 ( ) ) ;
Sebastian Junges
6 years ago