public AStarNode(AStarNode previousNode, VacuumAgent.VacuumAction action)
{
_action = action;
_state = new CustomEnvState(previousNode._state, action);
/// Calcul du cout associe a ce noeud
/// Le cout pour atteindre ce noeud est donnee par le coup des actions precedente
_costToRechNode = previousNode._costToRechNode;
if (previousNode._action == VacuumAgent.VacuumAction.GrabClean)
{
/// On ajoute 1 si l'action precedente est grab clean car cela represente deux actions
_costToRechNode += 2;
}
else
{
_costToRechNode += 1;
}
_costToReachSolution = _costToRechNode + (action == VacuumAgent.VacuumAction.Clean ? 0 : (action == VacuumAgent.VacuumAction.GrabClean ? 1 : _state.EuclidianActionHeuristic()));
}
public bool HasBeenSolved(CustomEnvState resultState)
{
return(_wishedState.IsEqualRelativeToTestedAttribute(resultState));
}
public Problem(CustomEnvState initialState, CustomEnvState wishedState)
{
_initialState = initialState;
_wishedState = wishedState;
}
public AStarNode(CustomEnvState initialState)
{
_state = initialState;
_costToRechNode = 0;
_costToReachSolution = _state.EuclidianActionHeuristic();
}
public static void VacuumProc()
{
// Attente de l'initialisation de l'environnement
while (!Environment._init)
{
}
Init();
Console.WriteLine(3 & Environment.DIRT);
Stack <VacuumAction> intent = new Stack <VacuumAction>();
_actionCycle = 0;
while (true)
{
if (intent.Count == 0 || _actionsCount >= _actionCycle)
{
// Récupération de l'état actuel de l'environnement
int[,] belief = Environment._grid;
CustomEnvState currentState = new CustomEnvState(belief, _pos);
// L'agent ne se déplace que si l'une des pièces est sale
if (currentState.NbOfDirtyRoom > 0)
{
if (_nextAlgo != _currentAlgorithm)
{
_currentAlgorithm = _nextAlgo;
MainWindow.Instance.Dispatcher.Invoke(() => MainWindow.Instance.UpdateAlgo(_currentAlgorithm.ToString()));
}
// Mesure de performance
if (_actionsCount != 0)
{
if (_learningCount >= _learningCycle - 1)
{
_optimalActionCycle = ComputeOptimalActionCycle();
MainWindow.Instance.Dispatcher.Invoke(() => MainWindow.Instance.UpdateOptimalActions());
_learningCount = 0;
}
else
{
_learningCount++;
}
_lastActionsCycleTrack.Add(new KeyValuePair <int, float>(_actionsCount, Environment.GivePerf()));
MainWindow.Instance.Dispatcher.Invoke(() => MainWindow.Instance.AddLearnedAction(_actionsCount, Environment.GivePerf()));
Environment.ResetPerf();
_actionsCount = 0;
}
// Formulation du but
// Nous définissons le but de cet agent comme étant de nettoyer une seule pièce
CustomEnvState wishedState = new CustomEnvState(belief, _pos);
wishedState.DefineWishedRoomDirtyAs(currentState.NbOfDirtyRoom - 1);
wishedState.MarkAttributeForEquality(CustomEnvState.NUMBER_DIRTY_ROOM_ATTRIBUTE);
// Formulation du problème
Problem problem = new Problem(currentState, wishedState);
// Exploration
intent = Explore(problem, _currentAlgorithm);
// Mise à jour du cycle d'action optimal
_actionCycle = _optimalActionCycle == 0 ? intent.Count : _optimalActionCycle + WeightedRandom(0, Math.Max(intent.Count - _optimalActionCycle, 0));
MainWindow.Instance.Dispatcher.Invoke(() => MainWindow.Instance.UpdateActionCycle());
MainWindow.Instance.Dispatcher.Invoke(() => MainWindow.Instance.UpdateComputingState(""));
}
}
else if (_actionsCount < _actionCycle)
{
_actionsCount++;
// Exécuter et retirer une action du plan d'actions
VacuumAction action = intent.Pop();
Execute(action);
Thread.Sleep(700);
}
}
}
public BFSNode(CustomEnvState previousState, VacuumAgent.VacuumAction action)
{
_action = action;
_state = new CustomEnvState(previousState, action);
}
public BFSNode(CustomEnvState initialState)
{
_state = initialState;
}
