#include "src/solver/GameSolver.h" #include "src/storage/SparseMatrix.h" #include "src/utility/vector.h" namespace storm { namespace solver { template <typename ValueType> GameSolver<ValueType>::GameSolver(storm::storage::SparseMatrix<storm::storage::sparse::state_type> const& player1Matrix, storm::storage::SparseMatrix<ValueType> const& player2Matrix) : AbstractGameSolver(), player1Matrix(player1Matrix), player2Matrix(player2Matrix) { // Intentionally left empty. } template <typename ValueType> GameSolver<ValueType>::GameSolver(storm::storage::SparseMatrix<storm::storage::sparse::state_type> const& player1Matrix, storm::storage::SparseMatrix<ValueType> const& player2Matrix, double precision, uint_fast64_t maximalNumberOfIterations, bool relative) : AbstractGameSolver(precision, maximalNumberOfIterations, relative), player1Matrix(player1Matrix), player2Matrix(player2Matrix) { // Intentionally left empty. } template <typename ValueType> void GameSolver<ValueType>::solveGame(OptimizationDirection player1Goal, OptimizationDirection player2Goal, std::vector<ValueType>& x, std::vector<ValueType> const& b) const { // Set up the environment for value iteration. uint_fast64_t numberOfPlayer1States = x.size(); bool converged = false; std::vector<ValueType> tmpResult(numberOfPlayer1States); std::vector<ValueType> nondetResult(player2Matrix.getRowCount()); std::vector<ValueType> player2Result(player2Matrix.getRowGroupCount()); // Now perform the actual value iteration. uint_fast64_t iterations = 0; do { player2Matrix.multiplyWithVector(x, nondetResult); storm::utility::vector::addVectors(b, nondetResult, nondetResult); if (player2Goal == OptimizationDirection::Minimize) { storm::utility::vector::reduceVectorMin(nondetResult, player2Result, player2Matrix.getRowGroupIndices()); } else { storm::utility::vector::reduceVectorMax(nondetResult, player2Result, player2Matrix.getRowGroupIndices()); } for (uint_fast64_t pl1State = 0; pl1State < numberOfPlayer1States; ++pl1State) { storm::storage::SparseMatrix<storm::storage::sparse::state_type>::const_rows relevantRows = player1Matrix.getRowGroup(pl1State); if (relevantRows.getNumberOfEntries() > 0) { storm::storage::SparseMatrix<storm::storage::sparse::state_type>::const_iterator it = relevantRows.begin(); storm::storage::SparseMatrix<storm::storage::sparse::state_type>::const_iterator ite = relevantRows.end(); // Set the first value. tmpResult[pl1State] = player2Result[it->getColumn()]; ++it; // Now iterate through the different values and pick the extremal one. if (player1Goal == OptimizationDirection::Minimize) { for (; it != ite; ++it) { tmpResult[pl1State] = std::min(tmpResult[pl1State], player2Result[it->getColumn()]); } } else { for (; it != ite; ++it) { tmpResult[pl1State] = std::max(tmpResult[pl1State], player2Result[it->getColumn()]); } } } else { tmpResult[pl1State] = storm::utility::zero<ValueType>(); } } // Check if the process converged and set up the new iteration in case we are not done. converged = storm::utility::vector::equalModuloPrecision(x, tmpResult, precision, relative); std::swap(x, tmpResult); ++iterations; } while (!converged && iterations < maximalNumberOfIterations); } template class GameSolver<double>; } }