Various fixes for under/over approximation with rewards.

src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.cpp

@@ -387,6 +387,7 @@ namespace storm {
                     hintVector[extraTargetState] = storm::utility::one<ValueType>();
                 }
                 std::vector<uint64_t> targetStates = {extraTargetState};
+                storm::storage::BitVector fullyExpandedStates;
 
                 // Map to save the weighted values resulting from the preprocessing for the beliefs / indices in beliefSpace
                 std::map<uint64_t, ValueType> weightedSumOverMap;
@@ -441,9 +442,8 @@ namespace storm {
                     beliefsToBeExpanded.pop_front();
                     
                     uint64_t currMdpState = beliefStateMap.left.at(currId);
-                    auto const& currBelief = beliefGrid.getGridPoint(currId);
-                    uint32_t currObservation = beliefGrid.getBeliefObservation(currBelief);
-                    
+                    uint32_t currObservation = beliefGrid.getBeliefObservation(currId);
+
                     mdpTransitionsBuilder.newRowGroup(mdpMatrixRow);
                     
                     if (targetObservations.count(currObservation) != 0) {
@@ -457,8 +457,9 @@ namespace storm {
                         mdpTransitionsBuilder.addNextValue(mdpMatrixRow, extraBottomState, storm::utility::one<ValueType>() - weightedSumOverMap[currId]);
                         ++mdpMatrixRow;
                     } else {
-                        auto const& currBelief = beliefGrid.getGridPoint(currId);
-                        uint64_t someState = currBelief.begin()->first;
+                        fullyExpandedStates.grow(nextMdpStateId, false);
+                        fullyExpandedStates.set(currMdpState, true);
+                        uint64_t someState = beliefGrid.getGridPoint(currId).begin()->first;
                         uint64_t numChoices = pomdp.getNumberOfChoices(someState);
 
                         for (uint64_t action = 0; action < numChoices; ++action) {
@@ -507,6 +508,7 @@ namespace storm {
                     statistics.overApproximationBuildTime.stop();
                     return nullptr;
                 }
+                fullyExpandedStates.resize(nextMdpStateId, false);
                 
                 storm::models::sparse::StateLabeling mdpLabeling(nextMdpStateId);
                 mdpLabeling.addLabel("init");
@@ -520,13 +522,15 @@ namespace storm {
                 if (computeRewards) {
                     storm::models::sparse::StandardRewardModel<ValueType> mdpRewardModel(boost::none, std::vector<ValueType>(mdpMatrixRow));
                     for (auto const &iter : beliefStateMap.left) {
-                        auto currentBelief = beliefGrid.getGridPoint(iter.first);
-                        auto representativeState = currentBelief.begin()->first;
-                        for (uint64_t action = 0; action < overApproxMdp->getNumberOfChoices(representativeState); ++action) {
-                            // Add the reward
-                            uint64_t mdpChoice = overApproxMdp->getChoiceIndex(storm::storage::StateActionPair(iter.second, action));
-                            uint64_t pomdpChoice = pomdp.getChoiceIndex(storm::storage::StateActionPair(representativeState, action));
-                            mdpRewardModel.setStateActionReward(mdpChoice, getRewardAfterAction(pomdpChoice, currentBelief));
+                        if (fullyExpandedStates.get(iter.second)) {
+                            auto currentBelief = beliefGrid.getGridPoint(iter.first);
+                            auto representativeState = currentBelief.begin()->first;
+                            for (uint64_t action = 0; action < pomdp.getNumberOfChoices(representativeState); ++action) {
+                                // Add the reward
+                                uint64_t mdpChoice = overApproxMdp->getChoiceIndex(storm::storage::StateActionPair(iter.second, action));
+                                uint64_t pomdpChoice = pomdp.getChoiceIndex(storm::storage::StateActionPair(representativeState, action));
+                                mdpRewardModel.setStateActionReward(mdpChoice, getRewardAfterAction(pomdpChoice, currentBelief));
+                            }
                         }
                     }
                     overApproxMdp->addRewardModel("default", mdpRewardModel);
@@ -1076,7 +1080,8 @@ namespace storm {
                     ++mdpMatrixRow;
                 }
                 std::vector<uint64_t> targetStates = {extraTargetState};
-
+                storm::storage::BitVector fullyExpandedStates;
+                
                 bsmap_type beliefStateMap;
                 std::deque<uint64_t> beliefsToBeExpanded;
                 
@@ -1106,11 +1111,11 @@ namespace storm {
                         mdpTransitionsBuilder.addNextValue(mdpMatrixRow, currMdpState, storm::utility::one<ValueType>());
                         ++mdpMatrixRow;
                     } else if (currMdpState > maxModelSize) {
-                        // In other cases, this could be helpflull as well.
                         if (min) {
                             // Get an upper bound here
                             if (computeRewards) {
                                 // TODO: With minimizing rewards we need an upper bound!
+                                // In other cases, this could be helpflull as well.
                                 // For now, add a selfloop to "generate" infinite reward
                                 mdpTransitionsBuilder.addNextValue(mdpMatrixRow, currMdpState, storm::utility::one<ValueType>());
                             } else {
@@ -1121,6 +1126,8 @@ namespace storm {
                         }
                         ++mdpMatrixRow;
                     } else {
+                        fullyExpandedStates.grow(nextMdpStateId, false);
+                        fullyExpandedStates.set(currMdpState, true);
                         // Iterate over all actions and add the corresponding transitions
                         uint64_t someState = currBelief.begin()->first;
                         uint64_t numChoices = pomdp.getNumberOfChoices(someState);
@@ -1153,7 +1160,7 @@ namespace storm {
                     statistics.underApproximationBuildTime.stop();
                     return nullptr;
                 }
-                
+                fullyExpandedStates.resize(nextMdpStateId, false);
                 storm::models::sparse::StateLabeling mdpLabeling(nextMdpStateId);
                 mdpLabeling.addLabel("init");
                 mdpLabeling.addLabel("target");
@@ -1167,13 +1174,15 @@ namespace storm {
                 if (computeRewards) {
                     storm::models::sparse::StandardRewardModel<ValueType> mdpRewardModel(boost::none, std::vector<ValueType>(mdpMatrixRow));
                     for (auto const &iter : beliefStateMap.left) {
-                        auto currentBelief = beliefGrid.getGridPoint(iter.first);
-                        auto representativeState = currentBelief.begin()->first;
-                        for (uint64_t action = 0; action < model->getNumberOfChoices(representativeState); ++action) {
-                            // Add the reward
-                            uint64_t mdpChoice = model->getChoiceIndex(storm::storage::StateActionPair(iter.second, action));
-                            uint64_t pomdpChoice = pomdp.getChoiceIndex(storm::storage::StateActionPair(representativeState, action));
-                            mdpRewardModel.setStateActionReward(mdpChoice, getRewardAfterAction(pomdpChoice, currentBelief));
+                        if (fullyExpandedStates.get(iter.second)) {
+                            auto currentBelief = beliefGrid.getGridPoint(iter.first);
+                            auto representativeState = currentBelief.begin()->first;
+                            for (uint64_t action = 0; action < pomdp.getNumberOfChoices(representativeState); ++action) {
+                                // Add the reward
+                                uint64_t mdpChoice = model->getChoiceIndex(storm::storage::StateActionPair(iter.second, action));
+                                uint64_t pomdpChoice = pomdp.getChoiceIndex(storm::storage::StateActionPair(representativeState, action));
+                                mdpRewardModel.setStateActionReward(mdpChoice, getRewardAfterAction(pomdpChoice, currentBelief));
+                            }
                         }
                     }
                     model->addRewardModel("default", mdpRewardModel);