heuristic + arena + alphabeta

javaAPI/fr/iut_fbleau/HexGame/MonteCarloBot.java

@@ -0,0 +1,59 @@
+package fr.iut_fbleau.HexGame;
+
+import fr.iut_fbleau.GameAPI.*;
+
+import java.util.Random;
+
+public class MonteCarloBot extends AbstractGamePlayer {
+
+    private static final int SIMULATION_COUNT = 1000;
+
+    public MonteCarloBot(Player me) {
+        super(me);  // Correct constructor usage
+    }
+
+    @Override
+    public AbstractPly giveYourMove(IBoard board) {
+        HexBoard hb = (HexBoard) board;
+        float bestScore = -Float.MAX_VALUE;
+        HexPly bestMove = null;
+
+        for (int i = 0; i < hb.getSize(); i++) {
+            for (int j = 0; j < hb.getSize(); j++) {
+                HexPly move = new HexPly(hb.getCurrentPlayer(), i, j);
+                if (hb.isLegal(move)) {
+                    hb.doPly(move);
+                    float score = monteCarloSimulation(hb);
+                    if (score > bestScore) {
+                        bestScore = score;
+                        bestMove = move;
+                    }
+                    hb.undoPly();
+                }
+            }
+        }
+        return bestMove;
+    }
+
+    private float monteCarloSimulation(HexBoard board) {
+        RandomBot simBot = new RandomBot(Player.PLAYER1, new Random().nextLong());
+        HexBoard simBoard = board.safeCopy();
+        int wins = 0;
+        int simulations = 0;
+
+        for (int i = 0; i < SIMULATION_COUNT; i++) {
+            while (!simBoard.isGameOver()) {
+                AbstractPly move = simBot.giveYourMove(simBoard);
+                simBoard.doPly(move);
+            }
+
+            if (simBoard.getResult() == Result.WIN) {
+                wins++;
+            }
+            simulations++;
+            simBoard = board.safeCopy(); // Reset the board for the next simulation
+        }
+
+        return (float) wins / simulations;
+    }
+}