You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 
 
 
 

254 lines
16 KiB

/*
* File: Regions.h
* Author: Tim Quatmann
*
* Created on November 16, 2015,
*
* This file provides functions to apply a solver on a nondeterministic model or a two player game.
* However, schedulers are used to compute an initial guess which will (hopefully) speed up the value iteration techniques.
*/
#ifndef STORM_UTILITY_POLICYGUESSING_H
#define STORM_UTILITY_POLICYGUESSING_H
#include "src/solver/GameSolver.h"
#include "src/solver/MinMaxLinearEquationSolver.h"
#include "src/solver/OptimizationDirection.h"
#include "src/utility/vector.h"
#include "src/storage/BitVector.h"
#include "src/storage/sparse/StateType.h"
#include "src/storage/SparseMatrix.h"
#include "src/storage/TotalScheduler.h"
namespace storm {
namespace utility{
namespace policyguessing {
/*!
* invokes the given game solver.
*
* The given schedulers for player 1 and player 2 will serve as initial guess.
* A linear equation system defined by the induced Matrix A and vector b is solved before
* solving the actual game.
* Note that, depending on the schedulers, the qualitative properties of the graph defined by A
* might be different to the original graph of the game.
* To ensure a unique solution, we need to filter out the "prob0"-states.
* To identify these states and set the result for them correctly, it is necessary to know whether rewards or probabilities are to be computed
*
* @param solver the solver to be invoked
* @param player1Goal Sets whether player 1 wants to minimize or maximize.
* @param player2Goal Sets whether player 2 wants to minimize or maximize.
* @param x The initial guess of the solution.
* @param b The vector to add after matrix-vector multiplication.
* @param player1Scheduler A Scheduler that selects rows in every rowgroup of player1. This will be used as an initial guess
* @param player2Scheduler A Scheduler that selects rows in every rowgroup of player2. This will be used as an initial guess
* @param targetChoices marks the choices in the player2 matrix that have a positive probability to lead to a target state
* @param prob0Value the value that, after Scheduler instantiation, is assigned to the states that have probability zero to reach a target
* @return The solution vector in the form of the vector x as well as the two schedulers.
*/
template<typename ValueType>
void solveGame( storm::solver::GameSolver<ValueType>& solver,
std::vector<ValueType>& x,
std::vector<ValueType> const& b,
OptimizationDirection player1Goal,
OptimizationDirection player2Goal,
storm::storage::TotalScheduler& player1Scheduler,
storm::storage::TotalScheduler& player2Scheduler,
storm::storage::BitVector const& targetChoices,
ValueType const& prob0Value
);
/*!
* invokes the given MinMaxLinearEquationSolver.
*
* The given Scheduler will serve as an initial guess.
* A linear equation system defined by the induced Matrix A and vector b is solved before
* solving the actual MinMax equation system.
* Note that, depending on the Scheduler, the qualitative properties of the graph defined by A
* might be different to the original graph.
* To ensure a unique solution, we need to filter out the "prob0"-states.
* To identify these states and set the result for them correctly, it is necessary to know whether rewards or probabilities are to be computed
*
* @param solver the solver that contains the matrix
* @param A The matrix itself
* @param x The initial guess of the solution.
* @param b The vector to add after matrix-vector multiplication.
* @param goal Sets whether we want to minimize or maximize.
* @param Scheduler A Scheduler that selects rows in every rowgroup.
* @param targetChoices marks the rows in the matrix that have a positive probability to lead to a target state
* @param prob0Value the value that is assigned to the states that have probability zero to reach a target
* @return The solution vector in the form of the vector x.
*/
template<typename ValueType>
void solveMinMaxLinearEquationSystem( storm::solver::MinMaxLinearEquationSolver<ValueType>& solver,
storm::storage::SparseMatrix<ValueType> const& A,
std::vector<ValueType>& x,
std::vector<ValueType> const& b,
OptimizationDirection goal,
storm::storage::TotalScheduler& Scheduler,
storm::storage::BitVector const& targetChoices,
ValueType const& prob0Value
);
/*!
* Constructs the equation system defined by the matrix inducedA and vector inducedB that result from applying
* the given schedulers to the matrices of the two players and the given b.
*
* Note that, depending on the schedulers, the qualitative properties of the graph defined by inducedA
* might be different to the original graph.
*
* @param solver the solver that contains the two player matrices
* @param b The vector in which to select the entries of the right hand side
* @param player1Scheduler A Scheduler that selects rows in every rowgroup of player1.
* @param player2Scheduler A Scheduler that selects rows in every rowgroup of player2.
* @param targetChoices marks the choices in the player2 matrix that have a positive probability to lead to a target state
* @param inducedA the Matrix for the resulting equation system
* @param inducedB the Vector for the resulting equation system
* @param probGreater0States marks the states which have a positive probability to lead to a target state
* @return Induced A, b and targets
*/
template<typename ValueType>
void getInducedEquationSystem(storm::solver::GameSolver<ValueType> const& solver,
std::vector<ValueType> const& b,
storm::storage::TotalScheduler const& player1Scheduler,
storm::storage::TotalScheduler const& player2Scheduler,
storm::storage::BitVector const& targetChoices,
storm::storage::SparseMatrix<ValueType>& inducedA,
std::vector<ValueType>& inducedB,
storm::storage::BitVector& probGreater0States
);
/*!
* Constructs the equation system defined by the matrix inducedA and vector inducedB that result from applying
* the given Scheduler to the matrix from the given solver and the given b.
*
* Note that, depending on the schedulers, the qualitative properties of the graph defined by inducedA
* might be different to the original graph.
*
* @param A the matrix
* @param b The vector in which to select the entries of the right hand side
* @param Scheduler A Scheduler that selects rows in every rowgroup.
* @param targetChoices marks the choices in the player2 matrix that have a positive probability to lead to a target state
* @param inducedA the Matrix for the resulting equation system
* @param inducedB the Vector for the resulting equation system
* @param probGreater0States marks the states which have a positive probability to lead to a target state
* @return Induced A, b and targets
*/
template<typename ValueType>
void getInducedEquationSystem(storm::storage::SparseMatrix<ValueType> const& A,
std::vector<ValueType> const& b,
storm::storage::TotalScheduler const& scheduler,
storm::storage::BitVector const& targetChoices,
storm::storage::SparseMatrix<ValueType>& inducedA,
std::vector<ValueType>& inducedB,
storm::storage::BitVector& probGreater0States
);
/*!
* Solves the given equation system.
*
* It is not assumed that qualitative properties of the Graph defined by A have been checked, yet.
* However, actual target states are already filtered out.
* To ensure a unique solution, we also need to filter out the "prob0"-states.
*
* If the result does not satisfy "Ax+b = x (modulo precision)", the solver is executed
* again with increased precision.
*
* @param A the matrix of the equation system
* @param x The initial guess of the solution.
* @param b The vector of the equation system
* @param targetChoices marks the rows in the matrix that have a positive probability to lead to a target state
* @param prob0Value the value that is assigned to the states that have probability zero to reach a target
* @param const& precision The precision to be used by the solver
* @param relative sets whether to consider relative errors
* @return The solution vector in the form of the vector x.
*/
template<typename ValueType>
void solveLinearEquationSystem(storm::storage::SparseMatrix<ValueType>const& A,
std::vector<ValueType>& x,
std::vector<ValueType> const& b,
storm::storage::BitVector const& probGreater0States,
ValueType const& prob0Value,
ValueType const& precision,
bool relative
);
/*!
* Checks if the given schedulers make choices that lead to states from which no target state is reachable ("prob0"-states).
* This can happen when value iteration is applied and there are multiple choices with the same value
* (e.g. a state that allows to chose a selfloop with probability one)
*
* If the schedulers are changed, they are updated accordingly (as well as the given inducedA, inducedB and probGreater0States)
*
* @param solver the solver that contains the two player matrices
* @param x the solution vector (the result from value iteration)
* @param b The vector in which to select the entries of the right hand side
* @param player1Scheduler A Scheduler that selects rows in every rowgroup of player1.
* @param player2Scheduler A Scheduler that selects rows in every rowgroup of player2.
* @param targetChoices marks the choices in the player2 matrix that have a positive probability to lead to a target state
* @param inducedA the Matrix for the equation system
* @param inducedB the Vector for the equation system
* @param probGreater0States marks the states which have a positive probability to lead to a target state
* @return true iff there are no more prob0-states. Also changes the given schedulers accordingly
*/
template<typename ValueType>
bool checkAndFixScheduler(storm::solver::GameSolver<ValueType> const& solver,
std::vector<ValueType> const& x,
std::vector<ValueType> const& b,
storm::storage::TotalScheduler& player1Scheduler,
storm::storage::TotalScheduler& player2Scheduler,
storm::storage::BitVector const& targetChoices,
storm::storage::SparseMatrix<ValueType>& inducedA,
std::vector<ValueType>& inducedB,
storm::storage::BitVector& probGreater0States
);
/*!
* Checks if the given schedulers make choices that lead to states from which no target state is reachable ("prob0"-states).
* This can happen when value iteration is applied and there are multiple choices with the same value
* (e.g. a state that allows to chose a selfloop with probability one)
*
* If the schedulers are changed, they are updated accordingly (as well as the given inducedA, inducedB and probGreater0States)
*
* @param A the matrix
* @param x the solution vector (the result from value iteration)
* @param b The vector in which to select the entries of the right hand side
* @param Scheduler A Scheduler that selects rows in every rowgroup.
* @param targetChoices marks the choices in the player2 matrix that have a positive probability to lead to a target state
* @param inducedA the Matrix for the equation system
* @param probGreater0States marks the states which have a positive probability to lead to a target state
* @return true iff there are no more prob0-states. Also changes the given schedulers accordingly
*/
template<typename ValueType>
bool checkAndFixScheduler(storm::storage::SparseMatrix<ValueType> const& A,
std::vector<ValueType> const& x,
std::vector<ValueType> const& b,
storm::storage::TotalScheduler& Scheduler,
storm::storage::BitVector const& targetChoices,
ValueType const& precision,
bool relative,
storm::storage::SparseMatrix<ValueType>& inducedA,
std::vector<ValueType>& inducedB,
storm::storage::BitVector& probGreater0States
);
//little helper function
template<typename ValueType>
bool rowLeadsToTarget(uint_fast64_t row,
storm::storage::SparseMatrix<ValueType> const& matrix,
storm::storage::BitVector const& targetChoices,
storm::storage::BitVector const& probGreater0States){
if(targetChoices.get(row)) return true;
for(auto const& successor : matrix.getRow(row)){
if(probGreater0States.get(successor.getColumn())) return true;
}
return false;
}
}
}
}
#endif /* STORM_UTILITY_REGIONS_H */