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changement dans la façon qu'il apprend

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guezoloic
2024-06-04 12:08:46 +02:00
parent b40750c4ed
commit 99c814c37f
6 changed files with 52 additions and 46 deletions
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2
Makefile
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@@ -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)

BIN
res/save/learn.ser
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res/save/learn1.ser Normal file
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58
src/Main.java
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@@ -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]);
}
}
}

38
src/tests/IATest.java
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@@ -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);
}
}