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Nondet Lra: Fixed LP implementation for Markov automata.

tempestpy_adaptions
Tim Quatmann 4 years ago
parent
9fbb587884
commit
68b4d8dbd2
2 changed files with 47 additions and 16 deletions
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  1. 62
      src/storm/modelchecker/helper/infinitehorizon/SparseNondeterministicInfiniteHorizonHelper.cpp
  2. 1
      src/storm/modelchecker/helper/infinitehorizon/SparseNondeterministicInfiniteHorizonHelper.h

62
src/storm/modelchecker/helper/infinitehorizon/SparseNondeterministicInfiniteHorizonHelper.cpp
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@ -81,6 +81,8 @@ namespace storm {
template <typename ValueType>
std::vector<ValueType> SparseNondeterministicInfiniteHorizonHelper<ValueType>::computeLongRunAverageValues(Environment const& env, std::function<ValueType(uint64_t stateIndex)> const& stateRewardsGetter, std::function<ValueType(uint64_t globalChoiceIndex)> const& actionRewardsGetter) {
// We will compute the long run average value for each MEC individually and then set-up a MinMax Equation system to compute the value also at non-mec states.
// For a description of this approach see, e.g., Guck et al.: Modelling and Analysis of Markov Reward Automata (ATVA'14), https://doi.org/10.1007/978-3-319-11936-6_13
// Prepare an environment for the underlying solvers
auto underlyingSolverEnvironment = env;
@ -232,42 +234,70 @@ namespace storm {
template <typename ValueType>
ValueType SparseNondeterministicInfiniteHorizonHelper<ValueType>::computeLraForMecLp(Environment const& env, std::function<ValueType(uint64_t stateIndex)> const& stateRewardsGetter, std::function<ValueType(uint64_t globalChoiceIndex)> const& actionRewardsGetter, storm::storage::MaximalEndComponent const& mec) {
std::shared_ptr<storm::solver::LpSolver<ValueType>> solver = storm::utility::solver::getLpSolver<ValueType>("LRA for MEC");
// Create an LP solver
auto solver = storm::utility::solver::LpSolverFactory<ValueType>().create("LRA for MEC");
// Now build the LP formulation as described in:
// Guck et al.: Modelling and Analysis of Markov Reward Automata (ATVA'14), https://doi.org/10.1007/978-3-319-11936-6_13
solver->setOptimizationDirection(invert(this->getOptimizationDirection()));
// First, we need to create the variables for the problem.
// Create variables
// TODO: Investigate whether we can easily make the variables bounded
std::map<uint_fast64_t, storm::expressions::Variable> stateToVariableMap;
for (auto const& stateChoicesPair : mec) {
std::string variableName = "h" + std::to_string(stateChoicesPair.first);
std::string variableName = "x" + std::to_string(stateChoicesPair.first);
stateToVariableMap[stateChoicesPair.first] = solver->addUnboundedContinuousVariable(variableName);
}
storm::expressions::Variable lambda = solver->addUnboundedContinuousVariable("L", 1);
storm::expressions::Variable k = solver->addUnboundedContinuousVariable("k", storm::utility::one<ValueType>());
solver->update();
// Now we encode the problem as constraints.
// Add constraints.
for (auto const& stateChoicesPair : mec) {
uint_fast64_t state = stateChoicesPair.first;
bool stateIsMarkovian = _markovianStates && _markovianStates->get(state);
// Now, based on the type of the state, create a suitable constraint.
// Now create a suitable constraint for each choice
// x_s {≤, ≥} -k/rate(s) + sum_s' P(s,act,s') * x_s' + (value(s)/rate(s) + value(s,act))
for (auto choice : stateChoicesPair.second) {
storm::expressions::Expression constraint = -lambda;
for (auto const& element : _transitionMatrix.getRow(choice)) {
constraint = constraint + stateToVariableMap.at(element.getColumn()) * solver->getConstant(element.getValue());
std::vector<storm::expressions::Expression> summands;
auto matrixRow = _transitionMatrix.getRow(choice);
summands.reserve(matrixRow.getNumberOfEntries() + 2);
// add -k/rate(s) (only if s is either a Markovian state or we have an MDP)
if (stateIsMarkovian) {
summands.push_back(-(k / solver->getManager().rational((*_exitRates)[state])));
} else if (!isContinuousTime()) {
summands.push_back(-k);
}
// add sum_s' P(s,act,s') * x_s'
for (auto const& element : matrixRow) {
summands.push_back(stateToVariableMap.at(element.getColumn()) * solver->getConstant(element.getValue()));
}
// add value for state and choice
ValueType value;
if (stateIsMarkovian) {
// divide state reward with exit rate
value = stateRewardsGetter(state) / (*_exitRates)[state] + actionRewardsGetter(choice);
} else if (!isContinuousTime()) {
// in discrete time models no scaling is needed
value = stateRewardsGetter(state) + actionRewardsGetter(choice);
} else {
// state is a probabilistic state of a Markov automaton. The state reward is not collected
value = actionRewardsGetter(choice);
}
constraint = solver->getConstant(stateRewardsGetter(state) + actionRewardsGetter(choice)) + constraint;
summands.push_back(solver->getConstant(value));
storm::expressions::Expression constraint;
if (this->minimize()) {
constraint = stateToVariableMap.at(state) <= constraint;
constraint = stateToVariableMap.at(state) <= storm::expressions::sum(summands);
} else {
constraint = stateToVariableMap.at(state) >= constraint;
constraint = stateToVariableMap.at(state) >= storm::expressions::sum(summands);
}
solver->addConstraint("state" + std::to_string(state) + "," + std::to_string(choice), constraint);
solver->addConstraint("s" + std::to_string(state) + "," + std::to_string(choice), constraint);
}
}
solver->optimize();
return solver->getContinuousValue(lambda);
STORM_LOG_THROW(!isProduceSchedulerSet(), storm::exceptions::NotImplementedException, "Scheduler extraction is not yet implemented for LP based LRA method.");
return solver->getContinuousValue(k);
}
/*!

1
src/storm/modelchecker/helper/infinitehorizon/SparseNondeterministicInfiniteHorizonHelper.h
View File

@ -100,6 +100,7 @@ namespace storm {
ValueType computeLraForMecVi(Environment const& env, std::function<ValueType(uint64_t stateIndex)> const& stateValuesGetter, std::function<ValueType(uint64_t globalChoiceIndex)> const& actionValuesGetter, storm::storage::MaximalEndComponent const& mec);
/*!
* As computeLraForMec but uses linear programming as a solution method (independent of what is set in env)
* @see Guck et al.: Modelling and Analysis of Markov Reward Automata (ATVA'14), https://doi.org/10.1007/978-3-319-11936-6_13
*/
ValueType computeLraForMecLp(Environment const& env, std::function<ValueType(uint64_t stateIndex)> const& stateValuesGetter, std::function<ValueType(uint64_t globalChoiceIndex)> const& actionValuesGetter, storm::storage::MaximalEndComponent const& mec);
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