本文整理匯總了Java中burlap.behavior.stochasticgames.solvers.CorrelatedEquilibriumSolver.getCorrelatedEQJointStrategy方法的典型用法代碼示例。如果您正苦於以下問題：Java CorrelatedEquilibriumSolver.getCorrelatedEQJointStrategy方法的具體用法？Java CorrelatedEquilibriumSolver.getCorrelatedEQJointStrategy怎麽用？Java CorrelatedEquilibriumSolver.getCorrelatedEQJointStrategy使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類burlap.behavior.stochasticgames.solvers.CorrelatedEquilibriumSolver的用法示例。
在下文中一共展示了CorrelatedEquilibriumSolver.getCorrelatedEQJointStrategy方法的4個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Java代碼示例。
示例1: computeColStrategy
import burlap.behavior.stochasticgames.solvers.CorrelatedEquilibriumSolver; //導入方法依賴的package包/類
@Override
public double[] computeColStrategy(double[][] rowPayoff,
double[][] colPayoff) {
double [][] jointStrategy;
if(this.objective != CorrelatedEquilibriumObjective.LIBERTARIAN){
jointStrategy = CorrelatedEquilibriumSolver.getCorrelatedEQJointStrategy(this.objective, rowPayoff, colPayoff);
return GeneralBimatrixSolverTools.marginalizeColPlayerStrategy(jointStrategy);
}
else{
//libertarian assumes row player request, so transpose matrices
jointStrategy = CorrelatedEquilibriumSolver.getCorrelatedEQJointStrategy(this.objective,
GeneralBimatrixSolverTools.transposeMatrix(rowPayoff), GeneralBimatrixSolverTools.transposeMatrix(colPayoff));
//return row since we transposed players
return GeneralBimatrixSolverTools.marginalizeRowPlayerStrategy(jointStrategy);
}
}
示例2: computeRowStrategy
import burlap.behavior.stochasticgames.solvers.CorrelatedEquilibriumSolver; //導入方法依賴的package包/類
@Override
public double[] computeRowStrategy(double[][] rowPayoff,
double[][] colPayoff) {
double [][] jointStrategy = CorrelatedEquilibriumSolver.getCorrelatedEQJointStrategy(this.objective, rowPayoff, colPayoff);
return GeneralBimatrixSolverTools.marginalizeRowPlayerStrategy(jointStrategy);
}
示例3: performBackup
import burlap.behavior.stochasticgames.solvers.CorrelatedEquilibriumSolver; //導入方法依賴的package包/類
@Override
public double performBackup(State s, String forAgent,
Map<String, SGAgentType> agentDefinitions, AgentQSourceMap qSourceMap) {
if(agentDefinitions.size() != 2){
throw new RuntimeException("Correlated Q only defined for two agents.");
}
String otherAgentName = null;
for(String aname : agentDefinitions.keySet()){
if(!aname.equals(forAgent)){
otherAgentName = aname;
break;
}
}
QSourceForSingleAgent forAgentQSource = qSourceMap.agentQSource(forAgent);
QSourceForSingleAgent otherAgentQSource = qSourceMap.agentQSource(otherAgentName);
List<GroundedSGAgentAction> forAgentGSAs = SGAgentAction.getAllApplicableGroundedActionsFromActionList(s, forAgent, agentDefinitions.get(forAgent).actions);
List<GroundedSGAgentAction> otherAgentGSAs = SGAgentAction.getAllApplicableGroundedActionsFromActionList(s, otherAgentName, agentDefinitions.get(otherAgentName).actions);
double [][] forPlayerPaoyff = new double[forAgentGSAs.size()][otherAgentGSAs.size()];
double [][] otherPlayerPaoyff = new double[forAgentGSAs.size()][otherAgentGSAs.size()];
for(int i = 0; i < forAgentGSAs.size(); i++){
for(int j = 0; j < otherAgentGSAs.size(); j++){
JointAction ja = new JointAction();
ja.addAction(forAgentGSAs.get(i));
ja.addAction(otherAgentGSAs.get(j));
double q1 = forAgentQSource.getQValueFor(s, ja).q;
double q2 = otherAgentQSource.getQValueFor(s, ja).q;
forPlayerPaoyff[i][j] = q1;
otherPlayerPaoyff[i][j] = q2;
}
}
double [][] jointActionProbs = CorrelatedEquilibriumSolver.getCorrelatedEQJointStrategy(this.objectiveType, forPlayerPaoyff, otherPlayerPaoyff);
double [] expectedValue = GeneralBimatrixSolverTools.expectedPayoffs(forPlayerPaoyff, otherPlayerPaoyff, jointActionProbs);
return expectedValue[0];
}
示例4: performBackup
import burlap.behavior.stochasticgames.solvers.CorrelatedEquilibriumSolver; //導入方法依賴的package包/類
@Override
public double performBackup(State s, int forAgent, List<SGAgentType> agentDefinitions, AgentQSourceMap qSourceMap) {
if(agentDefinitions.size() != 2){
throw new RuntimeException("CoCoQ only defined for two agents.");
}
int otherAgent = forAgent == 0 ? 1 : 0;
QSourceForSingleAgent forAgentQSource = qSourceMap.agentQSource(forAgent);
QSourceForSingleAgent otherAgentQSource = qSourceMap.agentQSource(otherAgent);
List<Action> forAgentGSAs = ActionUtils.allApplicableActionsForTypes(agentDefinitions.get(forAgent).actions, s);
List<Action> otherAgentGSAs = ActionUtils.allApplicableActionsForTypes(agentDefinitions.get(otherAgent).actions, s);
double [][] forPlayerPaoyff = new double[forAgentGSAs.size()][otherAgentGSAs.size()];
double [][] otherPlayerPaoyff = new double[forAgentGSAs.size()][otherAgentGSAs.size()];
for(int i = 0; i < forAgentGSAs.size(); i++){
for(int j = 0; j < otherAgentGSAs.size(); j++){
JointAction ja = new JointAction();
ja.setAction(forAgent, forAgentGSAs.get(i));
ja.setAction(otherAgent, otherAgentGSAs.get(j));
double q1 = forAgentQSource.getQValueFor(s, ja).q;
double q2 = otherAgentQSource.getQValueFor(s, ja).q;
forPlayerPaoyff[i][j] = q1;
otherPlayerPaoyff[i][j] = q2;
}
}
double [][] jointActionProbs = CorrelatedEquilibriumSolver.getCorrelatedEQJointStrategy(this.objectiveType, forPlayerPaoyff, otherPlayerPaoyff);
double [] expectedValue = GeneralBimatrixSolverTools.expectedPayoffs(forPlayerPaoyff, otherPlayerPaoyff, jointActionProbs);
return expectedValue[0];
}