Ia (#4)

* correction de probleme et ajout d'un quick multi

* correction de probleme

* changer le main

* programme fini~ pour l'ia

* correction de probleme

* Robot (#3)

* Robot qui marche pas encore

* Robot teubé mais marche bien, marche très bien en local mais problème en réseau

* Robot fini mais problème en réseau

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* Update README.md

---------

Co-authored-by: Cpt-Adok <theo.faria@laposte.net>
Co-authored-by: Cpt-Adok <126670243+Cpt-Adok@users.noreply.github.com>

* correction de probleme et ajout de learn.ser

---------

Co-authored-by: Cpt-Adok <theo.faria@laposte.net>
Co-authored-by: Cpt-Adok <126670243+Cpt-Adok@users.noreply.github.com>

src/tests/IATest.java

@@ -0,0 +1,149 @@
+package tests;
+
+import java.io.File;
+
+import IA.QTable;
+import IA.State;
+import environnements.Grid;
+import environnements.Map;
+import personnages.IAQLearning;
+import personnages.Personnage;
+import types.Mouvement;
+
+public class IATest {
+    private final static String path = "res" + File.separator + 
+                                    "save" + File.separator + 
+                                    "learn.ser";
+
+    public static void learnIA() {
+        double alpha = 0.1;
+        double gamma = 0.9; 
+        double epsilon = 1.0; 
+        double decay_rate = 0.995; 
+        double minEpsilon = 0.01;
+
+        int totalEpisodes = 1000;
+
+        Personnage.n = 2;
+
+        for(int episode = 0; episode < totalEpisodes; episode++) {
+            QTable qTable = new QTable();
+            IAQLearning iaqLearning = new IAQLearning(new int[] {0, 0}, qTable, alpha, gamma, epsilon);
+            Map map = new Map(20, 20);
+    
+            qTable.getValues(path);
+    
+            while (true) {
+                Map mapIA = new Map(map.getGrid()[0].length, map.getGrid().length);
+                mapIA.replaceGrid(map.getGrid());
+                
+                map.placePersonnages(iaqLearning);
+
+                State currentState = iaqLearning.getCurrentState(map.getGrid());
+                Mouvement mouvement = iaqLearning.bestMouvement(currentState);
+
+                iaqLearning.moveSnake(mouvement);
+
+                int[] coordinate = iaqLearning.getHeadCoordinate();
+
+                if(map.isGameOver(coordinate) || iaqLearning.applyEffects(map.getEffect(coordinate))) {
+                    iaqLearning.receiveReward(currentState, mouvement, -1.0, currentState);
+                    break;
+                }
+
+                mapIA.placePersonnages(iaqLearning);
+
+                State nextState = iaqLearning.getCurrentState(mapIA.getGrid());
+
+                iaqLearning.receiveReward(currentState, mouvement, 0.1, nextState);
+                iaqLearning.increaseRound();
+
+                mapIA.clearMap();
+                map.clearMap(); 
+            }
+
+            qTable.save(path);
+
+            epsilon = Math.max(minEpsilon, epsilon * decay_rate);
+            System.out.println("Episode : " + episode + " | Robot 1 States : " + qTable.getqValues().size());
+        }
+    }
+
+    public static void learnIAvsIA() {
+        double alpha = 0.1;
+        double gamma = 0.9;
+
+        double[] epsilon = new double[] {1.0,};
+        
+        double decay_rate = 0.995;
+        double minEpsilon = 0.01;
+
+        int totalEpisodes = 1000;
+
+        Personnage.n = 4;
+
+        for (int episode = 0; episode < totalEpisodes; episode++) {
+            QTable qTable = new QTable();
+
+            IAQLearning[] iaqLearnings = new IAQLearning[] {
+                new IAQLearning(new int[] {2, 2}, qTable, alpha, gamma, epsilon[0]),
+                new IAQLearning(new int[] {9, 19}, qTable, alpha, gamma, epsilon[1])
+            };
+
+            Map map = new Map(12, 22);
+            
+            boolean isGameOver = false;
+
+            qTable.getValues(path);
+
+            while(true) {
+                for (int i = 0; i < iaqLearnings.length; i++) {
+                    IAQLearning iaqLearning = iaqLearnings[i];
+
+                    Grid[][] gridMap = map.getGrid();
+                    Map mapIA = new Map(gridMap[0].length, gridMap.length);
+                    mapIA.replaceGrid(gridMap);
+
+                    for (IAQLearning value : iaqLearnings) {
+                        map.placePersonnages(value);
+                    }
+
+                    State currentState = iaqLearning.getCurrentState(map.getGrid());
+                    Mouvement mouvement = iaqLearning.bestMouvement(currentState);
+    
+                    iaqLearning.moveSnake(mouvement);
+
+                    int[] coordinate = iaqLearning.getHeadCoordinate();
+
+                    for (int[] snakeCoordinate : iaqLearnings[(i + 1) % 2].getCoordinate()) {
+                        if (coordinate[0] == snakeCoordinate[0] && coordinate[1] == snakeCoordinate[1]) {
+                            iaqLearning.receiveReward(currentState, mouvement, -10.0, currentState);
+                            iaqLearnings[(i + 1) % 2].receiveReward(currentState, mouvement, 10.0, currentState);
+                            break;
+                        }
+                    }
+
+                    mapIA.placePersonnages(iaqLearning);
+
+                    State nextState = iaqLearning.getCurrentState(mapIA.getGrid());
+                    iaqLearning.receiveReward(currentState, mouvement, -0.1, nextState);
+
+                    iaqLearning.increaseRound();
+
+                    mapIA.clearMap();
+                    map.clearMap();
+                }
+
+                if(isGameOver) break;
+            }
+
+            qTable.save(path);
+
+            for (int i = 0; i < epsilon.length; i++) {
+                epsilon[i] = Math.max(minEpsilon, epsilon[i] * decay_rate);
+            }
+
+            System.out.println("Episode: " + episode + " | Robot 1 States: " + qTable.getqValues().size());
+        }
+    }
+}