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added and finalized NondetGamehelper methods 

This still needs some better documentation for the introduced class
methods.

Also removed some debug printing.
tempestpy_adaptions
Stefan Pranger 4 years ago
parent
72da4ba12e
commit
28eb89f6ac
2 changed files with 96 additions and 14 deletions
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  1. 89
      src/storm/modelchecker/helper/infinitehorizon/SparseNondeterministicGameInfiniteHorizonHelper.cpp
  2. 21
      src/storm/modelchecker/helper/infinitehorizon/SparseNondeterministicGameInfiniteHorizonHelper.h

89
src/storm/modelchecker/helper/infinitehorizon/SparseNondeterministicGameInfiniteHorizonHelper.cpp
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@ -3,6 +3,7 @@
#include "storm/modelchecker/helper/infinitehorizon/internal/LraViHelper.h"
#include "storm/storage/SparseMatrix.h"
#include "storm/storage/MaximalEndComponentDecomposition.h"
#include "storm/storage/Scheduler.h"
#include "storm/solver/MinMaxLinearEquationSolver.h"
@ -26,25 +27,99 @@ namespace storm {
// Intentionally left empty.
}
template <typename ValueType>
std::vector<uint64_t> const& SparseNondeterministicGameInfiniteHorizonHelper<ValueType>::getProducedOptimalChoices() const {
STORM_LOG_ASSERT(this->isProduceSchedulerSet(), "Trying to get the produced optimal choices although no scheduler was requested.");
STORM_LOG_ASSERT(this->_producedOptimalChoices.is_initialized(), "Trying to get the produced optimal choices but none were available. Was there a computation call before?");
return this->_producedOptimalChoices.get();
}
template <typename ValueType>
std::vector<uint64_t>& SparseNondeterministicGameInfiniteHorizonHelper<ValueType>::getProducedOptimalChoices() {
STORM_LOG_ASSERT(this->isProduceSchedulerSet(), "Trying to get the produced optimal choices although no scheduler was requested.");
STORM_LOG_ASSERT(this->_producedOptimalChoices.is_initialized(), "Trying to get the produced optimal choices but none were available. Was there a computation call before?");
return this->_producedOptimalChoices.get();
}
template <typename ValueType>
storm::storage::Scheduler<ValueType> SparseNondeterministicGameInfiniteHorizonHelper<ValueType>::extractScheduler() const {
auto const& optimalChoices = getProducedOptimalChoices();
storm::storage::Scheduler<ValueType> scheduler(optimalChoices.size());
for (uint64_t state = 0; state < optimalChoices.size(); ++state) {
scheduler.setChoice(optimalChoices[state], state);
}
return scheduler;
}
template <typename ValueType>
void SparseNondeterministicGameInfiniteHorizonHelper<ValueType>::createDecomposition() {
STORM_LOG_THROW(false, storm::exceptions::InternalException, "Creating Decompositions of SMGs is currently not possible.");
// TODO This needs to be changed to return the whole model as one component as long as there is no overwritten version of MaximalEndComponentDecomposition for SMGs.
if (this->_longRunComponentDecomposition == nullptr) {
// The decomposition has not been provided or computed, yet.
if (this->_backwardTransitions == nullptr) {
this->_computedBackwardTransitions = std::make_unique<storm::storage::SparseMatrix<ValueType>>(this->_transitionMatrix.transpose(true));
this->_backwardTransitions = this->_computedBackwardTransitions.get();
}
this->_computedLongRunComponentDecomposition = std::make_unique<storm::storage::MaximalEndComponentDecomposition<ValueType>>(this->_transitionMatrix, *this->_backwardTransitions);
this->_longRunComponentDecomposition = this->_computedLongRunComponentDecomposition.get();
}
}
template <typename ValueType>
ValueType SparseNondeterministicGameInfiniteHorizonHelper<ValueType>::computeLraForComponent(Environment const& env, ValueGetter const& stateRewardsGetter, ValueGetter const& actionRewardsGetter, storm::storage::MaximalEndComponent const& component) {
std::vector<ValueType> SparseNondeterministicGameInfiniteHorizonHelper<ValueType>::computeLongRunAverageValues(Environment const& env, ValueGetter const& stateValuesGetter, ValueGetter const& actionValuesGetter) {
auto underlyingSolverEnvironment = env;
std::vector<ValueType> componentLraValues;
createDecomposition();
componentLraValues.reserve(this->_longRunComponentDecomposition->size());
for (auto const& c : *(this->_longRunComponentDecomposition)) {
componentLraValues.push_back(computeLraForComponent(underlyingSolverEnvironment, stateValuesGetter, actionValuesGetter, c));
}
return componentLraValues;
}
STORM_LOG_THROW(false, storm::exceptions::InternalException, "Computing values for LRA for SMGs components is currently not possible.");
template <typename ValueType>
ValueType SparseNondeterministicGameInfiniteHorizonHelper<ValueType>::computeLraForComponent(Environment const& env, ValueGetter const& stateRewardsGetter, ValueGetter const& actionRewardsGetter, storm::storage::MaximalEndComponent const& component) {
// Allocate memory for the nondeterministic choices.
if (this->isProduceSchedulerSet()) {
if (!this->_producedOptimalChoices.is_initialized()) {
this->_producedOptimalChoices.emplace();
}
this->_producedOptimalChoices->resize(this->_transitionMatrix.getRowGroupCount());
}
storm::solver::LraMethod method = env.solver().lra().getNondetLraMethod();
if (method == storm::solver::LraMethod::LinearProgramming) {
STORM_LOG_THROW(false, storm::exceptions::InvalidSettingsException, "Unsupported technique.");
} else if (method == storm::solver::LraMethod::ValueIteration) {
return computeLraVi(env, stateRewardsGetter, actionRewardsGetter, component);
} else {
STORM_LOG_THROW(false, storm::exceptions::InvalidSettingsException, "Unsupported technique.");
}
}
template <typename ValueType>
std::vector<ValueType> SparseNondeterministicGameInfiniteHorizonHelper<ValueType>::buildAndSolveSsp(Environment const& env, std::vector<ValueType> const& componentLraValues) {
STORM_LOG_THROW(false, storm::exceptions::InternalException, "buildAndSolveSsp not available for SMGs");
ValueType SparseNondeterministicGameInfiniteHorizonHelper<ValueType>::computeLraVi(Environment const& env, ValueGetter const& stateRewardsGetter, ValueGetter const& actionRewardsGetter, storm::storage::MaximalEndComponent const& mec) {
// Collect some parameters of the computation
ValueType aperiodicFactor = storm::utility::convertNumber<ValueType>(env.solver().lra().getAperiodicFactor());
std::vector<uint64_t>* optimalChoices = nullptr;
if (this->isProduceSchedulerSet()) {
optimalChoices = &this->_producedOptimalChoices.get();
}
// Now create a helper and perform the algorithm
if (this->isContinuousTime()) {
STORM_LOG_THROW(false, storm::exceptions::InternalException, "We cannot handle continuous time games.");
} else {
storm::modelchecker::helper::internal::LraViHelper<ValueType, storm::storage::MaximalEndComponent, storm::modelchecker::helper::internal::LraViTransitionsType::GameNondetTsNoIs> viHelper(mec, this->_transitionMatrix, aperiodicFactor);
return viHelper.performValueIteration(env, stateRewardsGetter, actionRewardsGetter, nullptr, &this->getOptimizationDirection(), optimalChoices);
}
}
template <typename ValueType>
std::vector<ValueType> SparseNondeterministicGameInfiniteHorizonHelper<ValueType>::computeLongRunAverageValues(Environment const& env, ValueGetter const& stateRewardsGetter, ValueGetter const& actionRewardsGetter) {
STORM_LOG_THROW(false, storm::exceptions::InternalException, "computeLongRunAverageValues not possible yet.");
std::vector<ValueType> SparseNondeterministicGameInfiniteHorizonHelper<ValueType>::buildAndSolveSsp(Environment const& env, std::vector<ValueType> const& componentLraValues) {
STORM_LOG_THROW(false, storm::exceptions::InternalException, "We do not create compositions for LRA for SMGs, solving a stochastic shortest path problem is not available.");
}

21
src/storm/modelchecker/helper/infinitehorizon/SparseNondeterministicGameInfiniteHorizonHelper.h
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@ -1,4 +1,5 @@
#pragma once
#include "storm/modelchecker/helper/infinitehorizon/SparseInfiniteHorizonHelper.h"
namespace storm {
@ -29,31 +30,37 @@ namespace storm {
*/
SparseNondeterministicGameInfiniteHorizonHelper(storm::storage::SparseMatrix<ValueType> const& transitionMatrix, std::vector<std::pair<std::string, uint_fast64_t>> const& player);
/*!
* TODO
/*! TODO
* Computes the long run average value given the provided state and action based rewards
* @param stateValuesGetter a function returning a value for a given state index
* @param actionValuesGetter a function returning a value for a given (global) choice index
* @return a value for each state
*/
std::vector<ValueType> computeLongRunAverageValues(Environment const& env, ValueGetter const& stateRewardsGetter, ValueGetter const& actionRewardsGetter);
std::vector<ValueType> computeLongRunAverageValues(Environment const& env, ValueGetter const& stateValuesGetter, ValueGetter const& actionValuesGetter) override;
/*!
* @pre before calling this, a computation call should have been performed during which scheduler production was enabled.
* @return the produced scheduler of the most recent call.
*/
//std::vector<uint64_t> const& getProducedOptimalChoices() const;
std::vector<uint64_t> const& getProducedOptimalChoices() const;
/*!
* @pre before calling this, a computation call should have been performed during which scheduler production was enabled.
* @return the produced scheduler of the most recent call.
*/
//std::vector<uint64_t>& getProducedOptimalChoices();
std::vector<uint64_t>& getProducedOptimalChoices();
/*!
* @pre before calling this, a computation call should have been performed during which scheduler production was enabled.
* @return a new scheduler containing optimal choices for each state that yield the long run average values of the most recent call.
*/
//storm::storage::Scheduler<ValueType> extractScheduler() const;
storm::storage::Scheduler<ValueType> extractScheduler() const;
void createDecomposition();
ValueType computeLraForComponent(Environment const& env, ValueGetter const& stateValuesGetter, ValueGetter const& actionValuesGetter, storm::storage::MaximalEndComponent const& component);
ValueType computeLraVi(Environment const& env, ValueGetter const& stateValuesGetter, ValueGetter const& actionValuesGetter, storm::storage::MaximalEndComponent const& mec);
void createDecomposition();
std::vector<ValueType> buildAndSolveSsp(Environment const& env, std::vector<ValueType> const& mecLraValues);
private:

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