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correction de probleme

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guezoloic
2024-05-31 10:54:08 +02:00
parent b053f78fc2
commit b40750c4ed
4 changed files with 76 additions and 69 deletions
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res/save/learn.ser
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1
src/IA/QLearning.java
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@@ -95,6 +95,7 @@ public class QLearning {
bestMouvement = mouvement; bestMouvement = mouvement;
} }
} }
return bestMouvement; return bestMouvement;
} }
} }

61
src/Main.java
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@@ -4,43 +4,50 @@ import IA.QTable;
import environnement.*; import environnement.*;
import game.Terminal; import game.Terminal;
import personnage.*; import personnage.*;
import tests.IATest;
public class Main { public class Main {
public static void main(String[] args) { 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 // // lancer en local
if (args.length < 2) { // if (args.length < 2) {
Grid[][] grid = map.getGrid(); // Grid[][] grid = map.getGrid();
// QTable qTable = new QTable(); // QTable qTable1 = new QTable();
// qTable.getValues("res" + File.separator + "save" + File.separator + "learn.ser"); // qTable1.getValues("path_to_save_qtable1.ser");
// Avant de jouer contre l'ia, vous pouvez essayer de l'entrainer avec la fonction tests.IATest.learnIAvsIA() // QTable qTable2 = new QTable();
// il jouera avec lui meme et mettra les sauvegardes dans le dossier learn.ser, // qTable2.getValues("path_to_save_qtable2.ser");
// Attention lors de l'apprentissage, ne pas couper le processus sinon vous allez perdre toute vos donnees // // Avant de jouer contre l'ia, vous pouvez essayer de l'entrainer avec la fonction tests.IATest.learnIAvsIA()
Personnage[] personnages = new Personnage[] { // // il jouera avec lui meme et mettra les sauvegardes dans le dossier learn.ser,
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),
};
// map.addObjectsRandomize(new Item[] {Item.FRAISE, Item.WALL}, 2); // // Attention lors de l'apprentissage, ne pas couper le processus sinon vous allez perdre toute vos donnees
// map.addObjects(Item.FRAISE, 2, 2); // 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),
// };
new Terminal(map, personnages).run(); // // map.addObjectsRandomize(new Item[] {Item.FRAISE, Item.WALL}, 2);
} // // map.addObjects(Item.FRAISE, 2, 2);
// lancer en ligne
else {
Personnage[] personnages = new Personnage[] {
new Player(new int[] {0, 0}, "Philippe Etchebest"),
};
new Terminal(map, personnages).run(args[0], args[1]); // new Terminal(map, personnages).run();
} // }
// // lancer en ligne
// else {
// Personnage[] personnages = new Personnage[] {
// new Player(new int[] {0, 0}, "Philippe Etchebest"),
// };
// new Terminal(map, personnages).run(args[0], args[1]);
// }
IATest.learnIAvsIA();
} }
} }

71
src/tests/IATest.java
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@@ -1,9 +1,11 @@
package tests; package tests;
import java.io.File; import java.io.File;
import java.util.Arrays;
import IA.QTable; import IA.QTable;
import IA.State; import IA.State;
import display.Display;
import environnement.Grid; import environnement.Grid;
import environnement.Map; import environnement.Map;
import personnage.IAQLearning; import personnage.IAQLearning;
@@ -22,14 +24,14 @@ public class IATest {
double decay_rate = 0.995; double decay_rate = 0.995;
double minEpsilon = 0.01; double minEpsilon = 0.01;
int totalEpisodes = 1000; int totalEpisodes = 200;
Personnage.n = 2; Personnage.n = 4;
for(int episode = 0; episode < totalEpisodes; episode++) { for(int episode = 0; episode < totalEpisodes; episode++) {
QTable qTable = new QTable(); QTable qTable = new QTable();
IAQLearning iaqLearning = new IAQLearning(new int[] {0, 0}, qTable, alpha, gamma, epsilon); IAQLearning iaqLearning = new IAQLearning(new int[] {2, 2}, qTable, alpha, gamma, epsilon);
Map map = new Map(20, 20); Map map = new Map(12, 22);
qTable.getValues(path); qTable.getValues(path);
@@ -72,37 +74,29 @@ public class IATest {
public static void learnIAvsIA() { public static void learnIAvsIA() {
double alpha = 0.1; double alpha = 0.1;
double gamma = 0.9; double gamma = 0.9;
double epsilon = 0.1;
double[] epsilon = new double[] {1.0,}; int maxEpisode = 1000;
double decay_rate = 0.995;
double minEpsilon = 0.01;
int totalEpisodes = 1000;
Personnage.n = 4; Personnage.n = 4;
for (int episode = 0; episode < totalEpisodes; episode++) { for (int episode = 0; episode < maxEpisode; episode++) {
QTable qTable = new QTable(); QTable qTable = new QTable();
qTable.getValues(path);
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); 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),
};
boolean isGameOver = false; boolean isGameOver = false;
qTable.getValues(path);
while(true) { while(true) {
for (int i = 0; i < iaqLearnings.length; i++) { for (int personnages = 0; personnages < iaqLearnings.length; personnages++) {
IAQLearning iaqLearning = iaqLearnings[i]; IAQLearning iaqLearning = iaqLearnings[personnages];
Map mapIA = new Map(map.getGrid()[0].length, map.getGrid().length);
Grid[][] gridMap = map.getGrid();
Map mapIA = new Map(gridMap[0].length, gridMap.length);
mapIA.replaceGrid(gridMap);
for (IAQLearning value : iaqLearnings) { for (IAQLearning value : iaqLearnings) {
map.placePersonnages(value); map.placePersonnages(value);
@@ -115,10 +109,20 @@ public class IATest {
int[] coordinate = iaqLearning.getHeadCoordinate(); int[] coordinate = iaqLearning.getHeadCoordinate();
for (int[] snakeCoordinate : iaqLearnings[(i + 1) % 2].getCoordinate()) { if (map.isGameOver(coordinate) || iaqLearning.applyEffects(map.getEffect(coordinate))) {
if (coordinate[0] == snakeCoordinate[0] && coordinate[1] == snakeCoordinate[1]) { iaqLearning.receiveReward(currentState, mouvement, -1000, currentState);
iaqLearning.receiveReward(currentState, mouvement, -10.0, currentState); isGameOver = true;
iaqLearnings[(i + 1) % 2].receiveReward(currentState, mouvement, 10.0, currentState); break;
}
int value = (personnages + 1) % 2;
for (int[] snakeCoordinate : iaqLearnings[value].getCoordinate()) {
if (Arrays.equals(coordinate, snakeCoordinate)) {
iaqLearnings[value].receiveReward(currentState, mouvement, 1000, currentState);
iaqLearning.receiveReward(currentState, mouvement, -500, currentState);
isGameOver = true;
break; break;
} }
} }
@@ -126,7 +130,7 @@ public class IATest {
mapIA.placePersonnages(iaqLearning); mapIA.placePersonnages(iaqLearning);
State nextState = iaqLearning.getCurrentState(mapIA.getGrid()); State nextState = iaqLearning.getCurrentState(mapIA.getGrid());
iaqLearning.receiveReward(currentState, mouvement, -0.1, nextState); iaqLearning.receiveReward(currentState, mouvement, -0.01, nextState);
iaqLearning.increaseRound(); iaqLearning.increaseRound();
@@ -135,15 +139,10 @@ public class IATest {
} }
if(isGameOver) break; if(isGameOver) break;
}
qTable.save(path); qTable.save(path);
for (int i = 0; i < epsilon.length; i++) { System.out.println("Episode: " + episode + " States: " + qTable.getqValues().size());
epsilon[i] = Math.max(minEpsilon, epsilon[i] * decay_rate); }
}
System.out.println("Episode: " + episode + " | Robot 1 States: " + qTable.getqValues().size());
} }
} }
} }