sp
/
tempest
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
Browse Source

Various fixes for under/over approximation with rewards.

tempestpy_adaptions
Tim Quatmann 5 years ago
parent
3887e8a979
commit
110453146d
1 changed files with 31 additions and 22 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 53
      src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.cpp

53
src/storm-pomdp/modelchecker/ApproximatePOMDPModelchecker.cpp
View File

@ -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);

Write Preview
Loading…
Cancel
Save