diff --git a/Makefile b/Makefile
index 70717fc..7dd4b07 100644
--- a/Makefile
+++ b/Makefile
@@ -27,7 +27,7 @@ $(BIN_DIR)/$(MAIN_FILE).class : $(SRC_DIR)/$(MAIN_FILE).java
 	$(JAVAC) -d $(BIN_DIR) -sourcepath $(SRC_DIR) -classpath $(JAR) $<
 
 run:
-	java -cp $(BIN_DIR) $(MAIN_FILE) $(channel) $(adversaire)
+	java -Xmx16g -cp $(BIN_DIR) $(MAIN_FILE) $(channel) $(adversaire)
 
 clean:
 	@rm -rf $(BIN_DIR)
\ No newline at end of file
diff --git a/res/save/learn.ser b/res/save/learn.ser
deleted file mode 100644
index 8a51ba3..0000000
Binary files a/res/save/learn.ser and /dev/null differ
diff --git a/res/save/learn1.ser b/res/save/learn1.ser
new file mode 100644
index 0000000..c597512
Binary files /dev/null and b/res/save/learn1.ser differ
diff --git a/res/save/learn2.ser b/res/save/learn2.ser
new file mode 100644
index 0000000..c597512
Binary files /dev/null and b/res/save/learn2.ser differ
diff --git a/src/Main.java b/src/Main.java
index d863528..44b6336 100644
--- a/src/Main.java
+++ b/src/Main.java
@@ -8,46 +8,44 @@ import tests.IATest;
 
 public class Main {
     public static void main(String[] args) {
-        // Personnage.n = 4;
+        Personnage.n = 4;
 
-        // Map map = new Map(12, 22);
+        Map map = new Map(12, 22);
 
         // // lancer en local
-        // if (args.length < 2) {
-        //     Grid[][] grid = map.getGrid();
+        if (args.length < 2) {
+            Grid[][] grid = map.getGrid();
 
-        //     QTable qTable1 = new QTable();
-        //     qTable1.getValues("path_to_save_qtable1.ser");
+            // QTable qTable1 = new QTable();
+            // qTable1.getValues("res" + File.separator + "save" + File.separator + "learn1.ser");
 
-        //     QTable qTable2 = new QTable();
-        //     qTable2.getValues("path_to_save_qtable2.ser");
+            // QTable qTable2 = new QTable();
+            // qTable2.getValues("res" + File.separator + "save" + File.separator + "learn1.ser");
 
-        //     // Avant de jouer contre l'ia, vous pouvez essayer de l'entrainer avec la fonction tests.IATest.learnIAvsIA()
-        //     // il jouera avec lui meme et mettra les sauvegardes dans le dossier learn.ser, 
+            // // Avant de jouer contre l'ia, vous pouvez essayer de l'entrainer avec la fonction tests.IATest.learnIAvsIA()
+            // // il jouera avec lui meme et mettra les sauvegardes dans le dossier learn.ser, 
 
-        //     // Attention lors de l'apprentissage, ne pas couper le processus sinon vous allez perdre toute vos donnees
-        //     Personnage[] personnages = new Personnage[] {
-        //         new IAQLearning(new int[] {2, 2}, qTable1),
-        //         // new Player(new int[] {2, 2}, "Philippe Etchebest"),
-        //         // new Player(new int[] {grid[0].length - 3, grid.length - 3}, "Luke Skywalker"),
-        //         // new Robot("Robot", new int[] {grid[0].length - 3, grid.length - 3}),
-        //         new IAQLearning(new int[] {grid[0].length - 3, grid.length - 3}, qTable2),
-        //     };
+            // // Attention lors de l'apprentissage, ne pas couper le processus sinon vous allez perdre toute vos donnees
+            Personnage[] personnages = new Personnage[] {
+                // new IAQLearning(new int[] {2, 2}, qTable1),
+                new Player(new int[] {2, 2}, "Philippe Etchebest"),
+                new Player(new int[] {grid[0].length - 3, grid.length - 3}, "Luke Skywalker"),
+                // new Robot("Robot", new int[] {grid[0].length - 3, grid.length - 3}),
+                // new IAQLearning(new int[] {grid[0].length - 3, grid.length - 3}, qTable2),
+            };
 
-        //     // map.addObjectsRandomize(new Item[] {Item.FRAISE, Item.WALL}, 2);
-        //     // map.addObjects(Item.FRAISE, 2, 2);
+            // map.addObjectsRandomize(new Item[] {Item.FRAISE, Item.WALL}, 2);
+            // map.addObjects(Item.FRAISE, 2, 2);
 
-        //     new Terminal(map, personnages).run();
-        // } 
+            new Terminal(map, personnages).run();
+        } 
         // // lancer en ligne
-        // else {
-        //     Personnage[] personnages = new Personnage[] {
-        //         new Player(new int[] {0, 0}, "Philippe Etchebest"),
-        //     };
+        else {
+            Personnage[] personnages = new Personnage[] {
+                new Player(new int[] {0, 0}, "Philippe Etchebest"),
+            };
 
-        //     new Terminal(map, personnages).run(args[0], args[1]);
-        // }
-
-        IATest.learnIAvsIA();
+            new Terminal(map, personnages).run(args[0], args[1]);
+        }
     }
 }
diff --git a/src/tests/IATest.java b/src/tests/IATest.java
index dc3860c..f116e32 100644
--- a/src/tests/IATest.java
+++ b/src/tests/IATest.java
@@ -13,9 +13,13 @@ import personnage.Personnage;
 import types.Mouvement;
 
 public class IATest {
-    private final static String path = "res" + File.separator + 
+    private final static String path1 = "res" + File.separator + 
                                     "save" + File.separator + 
-                                    "learn.ser";
+                                    "learn1.ser";
+
+    private final static String path2 = "res" + File.separator + 
+                                    "save" + File.separator + 
+                                    "learn2.ser";
 
     public static void learnIA() {
         double alpha = 0.1;
@@ -33,7 +37,7 @@ public class IATest {
             IAQLearning iaqLearning = new IAQLearning(new int[] {2, 2}, qTable, alpha, gamma, epsilon);
             Map map = new Map(12, 22);
     
-            qTable.getValues(path);
+            qTable.getValues(path1);
     
             while (true) {
                 Map mapIA = new Map(map.getGrid()[0].length, map.getGrid().length);
@@ -64,31 +68,34 @@ public class IATest {
                 map.clearMap(); 
             }
 
-            qTable.save(path);
+            qTable.save(path1);
 
             epsilon = Math.max(minEpsilon, epsilon * decay_rate);
-            System.out.println("Episode : " + episode + " | Robot 1 States : " + qTable.getqValues().size());
+            System.out.println("Episode : " + episode + " | States : " + qTable.getqValues().size());
         }
     }
 
     public static void learnIAvsIA() {
-        double alpha = 0.1;
+        double alpha = 0.9;
         double gamma = 0.9;
         double epsilon = 0.1;
 
-        int maxEpisode = 1000;
+        int maxEpisode = 1000000;
 
         Personnage.n = 4;
 
-        for (int episode = 0; episode < maxEpisode; episode++) {
-            QTable qTable = new QTable();
-            qTable.getValues(path);
+        QTable qTable1 = new QTable();
+        qTable1.getValues(path1);
 
+        QTable qTable2 = new QTable();
+        qTable2.getValues(path2);
+
+        for (int episode = 0; episode < maxEpisode; episode++) {
             Map map = new Map(12, 22);
 
             IAQLearning[] iaqLearnings = new IAQLearning[] {
-                new IAQLearning(new int[] {2, 2}, qTable, alpha, gamma, epsilon),
-                new IAQLearning(new int[] {9, 19}, qTable, alpha, gamma, epsilon),
+                new IAQLearning(new int[] {2, 2}, qTable1, alpha, gamma, epsilon),
+                new IAQLearning(new int[] {9, 19}, qTable2, alpha, gamma, epsilon),
             };
 
             boolean isGameOver = false;
@@ -136,13 +143,14 @@ public class IATest {
 
                     mapIA.clearMap();
                     map.clearMap();
+
+                    System.out.println("States 1: " + qTable1.getqValues().size() + " States 2: " + qTable2.getqValues().size());
                 }
             
                 if(isGameOver) break;
-                qTable.save(path);
-
-                System.out.println("Episode: " + episode + " States: " + qTable.getqValues().size());
             }
+            System.out.println(" States 1: " + qTable1.getqValues().size() + " States 2: " + qTable2.getqValues().size() + "Episode: " + episode);
         }
+        qTable1.save(path1);
     }
 }