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bitboard merge

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This commit is contained in:
Ticho Hidding
2025-12-15 10:28:33 +01:00
parent ffdec38e5d
commit 5501c3893f
8 changed files with 234 additions and 152 deletions
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9
game/src/main/java/org/toop/game/games/reversi/BitboardReversi.java
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@@ -167,4 +167,13 @@ public class BitboardReversi extends BitboardGame<BitboardReversi> {
private long shift(long bit, int shift, long mask) {
return shift > 0 ? (bit << shift) & mask : (bit >>> -shift) & mask;
}
public boolean isGameOver(){
BitboardReversi copy = this.deepCopy();
if (copy.getLegalMoves() == 0){
nextTurn();
return copy.getLegalMoves() == 0;
}
return false;
}
}

57
game/src/main/java/org/toop/game/games/reversi/ReversiAIML.java
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@@ -1,57 +0,0 @@
package org.toop.game.games.reversi;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.util.ModelSerializer;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.toop.framework.gameFramework.model.player.AbstractAI;
import java.io.IOException;
import java.io.InputStream;
import static java.lang.Math.random;
public class ReversiAIML extends AbstractAI<ReversiR> {
MultiLayerNetwork model;
public ReversiAIML() {
InputStream is = getClass().getResourceAsStream("/reversi-model.zip");
try {
assert is != null;
model = ModelSerializer.restoreMultiLayerNetwork(is);
} catch (IOException e) {}
}
private int pickLegalMove(INDArray prediction, ReversiR reversi) {
double[] logits = prediction.toDoubleVector();
int[] legalMoves = reversi.getLegalMoves();
if (legalMoves.length == 0) return -1;
int bestMove = legalMoves[0];
double bestVal = logits[bestMove];
if (random() < 0.01){
return legalMoves[(int)(random()*legalMoves.length-.5)];
}
for (int move : legalMoves) {
if (logits[move] > bestVal) {
bestMove = move;
bestVal = logits[move];
}
}
return bestMove;
}
@Override
public int getMove(ReversiR game) {
int[] input = game.getBoard();
INDArray boardInput = Nd4j.create(new int[][] { input });
INDArray prediction = model.output(boardInput);
int move = pickLegalMove(prediction,game);
return move;
}
}

58
game/src/main/java/org/toop/game/games/reversi/ReversiAISimple.java
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@@ -1,58 +0,0 @@
package org.toop.game.games.reversi;
import org.toop.framework.gameFramework.model.player.AbstractAI;
import java.awt.*;
public class ReversiAISimple extends AbstractAI<ReversiR> {
private int getNumberOfOptions(ReversiR game, int move){
ReversiR copy = game.deepCopy();
copy.play(move);
return copy.getLegalMoves().length;
}
private int getScore(ReversiR game, int move){
return game.getFlipsForPotentialMove(new Point(move%game.getColumnSize(),move/game.getRowSize()),game.getCurrentTurn()).length;
}
@Override
public int getMove(ReversiR game) {
int[] moves = game.getLegalMoves();
int bestMove;
int bestMoveScore = moves[0];
int bestMoveOptions = moves[0];
int bestScore = -1;
int bestOptions = -1;
for (int move : moves){
int numOpt = getNumberOfOptions(game, move);
if (numOpt > bestOptions) {
bestOptions = numOpt;
bestMoveOptions = move;
}
int numSco = getScore(game, move);
if (numSco > bestScore) {
bestScore = numSco;
bestMoveScore = move;
}
if (numSco == bestScore || numOpt == bestOptions) {
if (Math.random() < 0.5) {
bestMoveOptions = move;
bestMoveScore = move;
}
}
//IO.println("Move: " + move.position() + ". Options: " + numOpt + ". Score: " + numSco);
}
if (bestScore > bestOptions) {
bestMove = bestMoveScore;
}
else{
bestMove = bestMoveOptions;
}
return bestMove;
}
}

98
game/src/main/java/org/toop/game/machinelearning/NeuralNetwork.java
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@@ -17,10 +17,11 @@ import org.toop.framework.gameFramework.GameState;
import org.toop.framework.gameFramework.model.game.PlayResult;
import org.toop.framework.gameFramework.model.player.AbstractAI;
import org.toop.framework.gameFramework.model.player.Player;
import org.toop.game.games.reversi.ReversiAIR;
import org.toop.game.games.reversi.ReversiR;
import org.toop.game.games.reversi.ReversiAIML;
import org.toop.game.games.reversi.ReversiAISimple;
import org.toop.game.games.reversi.BitboardReversi;
import org.toop.game.players.ArtificialPlayer;
import org.toop.game.players.ai.MiniMaxAI;
import org.toop.game.players.ai.RandomAI;
import org.toop.game.players.ai.ReversiAIML;
import java.io.File;
import java.io.IOException;
@@ -34,15 +35,18 @@ public class NeuralNetwork {
private MultiLayerConfiguration conf;
private MultiLayerNetwork model;
private AbstractAI<ReversiR> opponentAI;
private AbstractAI<ReversiR> opponentRand = new ReversiAIR();
private AbstractAI<ReversiR> opponentSimple = new ReversiAISimple();
private AbstractAI<ReversiR> opponentAIML = new ReversiAIML();
private AbstractAI<BitboardReversi> opponentAI;
private AbstractAI<BitboardReversi> opponentMM = new MiniMaxAI<>(6);
private AbstractAI<BitboardReversi> opponentRand = new RandomAI<>();
private AbstractAI<BitboardReversi> opponentAIML = new ReversiAIML<>();
private Player[] playerSet = new Player[4];
public NeuralNetwork() {}
public void init(){
initPlayers();
conf = new NeuralNetConfiguration.Builder()
.updater(new Adam(0.001))
.weightInit(WeightInit.XAVIER) //todo understand
@@ -70,6 +74,12 @@ public class NeuralNetwork {
saveModel();
}
public void initPlayers(){
playerSet[0] = new ArtificialPlayer<>(new MiniMaxAI<BitboardReversi>(6),"MiniMaxAI");
playerSet[1] = new ArtificialPlayer<>(new RandomAI<BitboardReversi>(),"RandomAI");
playerSet[2] = new ArtificialPlayer<>(new ReversiAIML<BitboardReversi>(),"MachineLearningAI");
}
public void saveModel(){
File modelFile = new File("reversi-model.zip");
try {
@@ -92,11 +102,13 @@ public class NeuralNetwork {
int totalGames = 5000;
double epsilon = 0.05;
long start = System.nanoTime();
for (int game = 0; game<totalGames; game++){
char modelPlayer = random()<0.5?'B':'W';
ReversiR reversi = new ReversiR(new Player[2]);
BitboardReversi reversi = new BitboardReversi(new Player[2]);
opponentAI = getOpponentAI();
List<StateAction> gameHistory = new ArrayList<>();
PlayResult state = new PlayResult(GameState.NORMAL,reversi.getCurrentTurn());
@@ -105,14 +117,14 @@ public class NeuralNetwork {
while (state.state() != GameState.DRAW && state.state() != GameState.WIN){
int curr = reversi.getCurrentTurn();
int move;
long move;
if (curr == modelPlayer) {
int[] input = reversi.getBoard();
long[] input = reversi.getBoard();
if (Math.random() < epsilon) {
int[] moves = reversi.getLegalMoves();
move = moves[(int) (Math.random() * moves.length - .5f)];
long moves = reversi.getLegalMoves();
move = (long) (Math.random() * Long.bitCount(moves) - .5f);
} else {
INDArray boardInput = Nd4j.create(new int[][]{input});
INDArray boardInput = Nd4j.create(new long[][]{input});
INDArray prediction = model.output(boardInput);
int location = pickLegalMove(prediction, reversi);
@@ -126,11 +138,11 @@ public class NeuralNetwork {
}
//IO.println(model.params());
ReversiR.Score score = reversi.getScore();
int scoreDif = abs(score.player1Score() - score.player2Score());
if (score.player1Score() > score.player2Score()){
BitboardReversi.Score score = reversi.getScore();
int scoreDif = abs(score.black() - score.white());
if (score.black() > score.white()){
reward = 1 + ((scoreDif / 64.0) * 0.5);
}else if (score.player1Score() < score.player2Score()){
}else if (score.black() < score.white()){
reward = -1 - ((scoreDif / 64.0) * 0.5);
}else{
reward = 0;
@@ -156,28 +168,46 @@ public class NeuralNetwork {
}
private int pickLegalMove(INDArray prediction, ReversiR reversi){
double[] probs = prediction.toDoubleVector();
int[] legalMoves = reversi.getLegalMoves();
private int pickLegalMove(INDArray prediction, BitboardReversi reversi) {
double[] logits = prediction.toDoubleVector();
long legalMoves = reversi.getLegalMoves();
if (legalMoves.length == 0) return -1;
int bestMove = legalMoves[0];
double bestVal = probs[bestMove];
for (int move : legalMoves){
if (probs[move] > bestVal){
bestMove = move;
bestVal = probs[bestMove];
}
if (legalMoves == 0L) {
return -1;
}
if (Math.random() < 0.01) {
int randomIndex = (int) (Math.random() * Long.bitCount(legalMoves));
long moves = legalMoves;
for (int i = 0; i < randomIndex; i++) {
moves &= moves - 1;
}
return Long.numberOfTrailingZeros(moves);
}
int bestMove = -1;
double bestVal = Double.NEGATIVE_INFINITY;
long moves = legalMoves;
while (moves != 0L) {
int move = Long.numberOfTrailingZeros(moves);
double value = logits[move];
if (value > bestVal) {
bestVal = value;
bestMove = move;
}
moves &= moves - 1;
}
return bestMove;
}
private AbstractAI<ReversiR> getOpponentAI(){
private AbstractAI<BitboardReversi> getOpponentAI(){
return switch ((int) (Math.random() * 4)) {
case 0 -> opponentRand;
case 1 -> opponentSimple;
case 1 -> opponentMM;
case 2 -> opponentAIML;
default -> opponentRand;
};
@@ -188,7 +218,7 @@ public class NeuralNetwork {
output[step.action] = reward;
DataSet ds = new DataSet(
Nd4j.create(new int[][] { step.state }),
Nd4j.create(new long[][] { step.state }),
Nd4j.create(new double[][] { output })
);

4
game/src/main/java/org/toop/game/machinelearning/StateAction.java
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@@ -1,9 +1,9 @@
package org.toop.game.machinelearning;
public class StateAction {
int[] state;
long[] state;
int action;
public StateAction(int[] state, int action) {
public StateAction(long[] state, int action) {
this.state = state;
this.action = action;
}

80
game/src/main/java/org/toop/game/players/ai/ReversiAIML.java Normal file
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@@ -0,0 +1,80 @@
package org.toop.game.players.ai;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.util.ModelSerializer;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.toop.framework.gameFramework.model.game.TurnBasedGame;
import org.toop.framework.gameFramework.model.player.AI;
import org.toop.framework.gameFramework.model.player.AbstractAI;
import org.toop.game.games.reversi.BitboardReversi;
import java.io.IOException;
import java.io.InputStream;
import static java.lang.Math.random;
public class ReversiAIML<T extends TurnBasedGame<T>> extends AbstractAI<T> {
MultiLayerNetwork model;
public ReversiAIML() {
InputStream is = getClass().getResourceAsStream("/reversi-model.zip");
try {
assert is != null;
model = ModelSerializer.restoreMultiLayerNetwork(is);
} catch (IOException e) {}
}
private int pickLegalMove(INDArray prediction, BitboardReversi reversi) {
double[] logits = prediction.toDoubleVector();
long legalMoves = reversi.getLegalMoves();
if (legalMoves == 0L) {
return -1;
}
if (Math.random() < 0.01) {
int randomIndex = (int) (Math.random() * Long.bitCount(legalMoves));
long moves = legalMoves;
for (int i = 0; i < randomIndex; i++) {
moves &= moves - 1;
}
return Long.numberOfTrailingZeros(moves);
}
int bestMove = -1;
double bestVal = Double.NEGATIVE_INFINITY;
long moves = legalMoves;
while (moves != 0L) {
int move = Long.numberOfTrailingZeros(moves);
double value = logits[move];
if (value > bestVal) {
bestVal = value;
bestMove = move;
}
moves &= moves - 1;
}
return bestMove;
}
@Override
public long getMove(T game) {
long[] input = game.getBoard();
INDArray boardInput = Nd4j.create(new long[][] { input });
INDArray prediction = model.output(boardInput);
int move = pickLegalMove(prediction,(BitboardReversi) game);
return move;
}
@Override
public ReversiAIML<T> deepCopy() {
return new ReversiAIML();
}
}

2
game/src/test/java/org/toop/game/tictactoe/ReversiTest.java
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@@ -17,7 +17,7 @@ import org.toop.game.games.reversi.ReversiR;
import org.toop.game.records.Move;
import org.toop.game.reversi.Reversi;
import org.toop.game.reversi.ReversiAI;
import org.toop.game.games.reversi.ReversiAIML;
import org.toop.game.players.ai.ReversiAIML;
import org.toop.game.games.reversi.ReversiAISimple;
import static org.junit.jupiter.api.Assertions.*;

78
game/src/test/java/org/toop/game/tictactoe/TestReversi.java Normal file
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@@ -0,0 +1,78 @@
package org.toop.game.tictactoe;
import java.util.*;
import org.junit.jupiter.api.BeforeEach;
import org.junit.jupiter.api.Test;
import org.toop.framework.gameFramework.model.player.Player;
import org.toop.game.games.reversi.BitboardReversi;
import org.toop.game.players.ArtificialPlayer;
import org.toop.game.players.ai.MiniMaxAI;
import org.toop.game.players.ai.RandomAI;
import static org.junit.jupiter.api.Assertions.*;
public class TestReversi {
private BitboardReversi game;
private Player[] players;
@BeforeEach
void setup(){
players = new Player[2];
players[0] = new ArtificialPlayer<BitboardReversi>(new RandomAI<BitboardReversi>(),"randomAI");
players[1] = new ArtificialPlayer<BitboardReversi>(new MiniMaxAI<BitboardReversi>(10),"miniMaxAI");
game = new BitboardReversi(players);
}
@Test
void testCorrectStartPiecesPlaced() {
assertNotNull(game);
long[] board = game.getBoard();
IO.println(Long.toBinaryString(board[0]));
IO.println(Long.toBinaryString(board[1]));
long black = board[0];
long white = board[1];
assertEquals(1L, ((white >>> 27) & 1L)); //checks if the 27-shifted long has a 1 bit
assertEquals(1L, ((black >>> 28) & 1L));
assertEquals(1L, ((black >>> 35) & 1L));
assertEquals(1L, ((white >>> 36) & 1L));
}
@Test
void testPlayGames(){
int totalGames = 1;
long start = System.nanoTime();
long midtime = System.nanoTime();
int p1wins = 0;
int p2wins = 0;
int draws = 0;
for (int i = 0; i < totalGames; i++){
game = new BitboardReversi(players);
while(!game.isGameOver()){
midtime = System.nanoTime();
int currentTurn = game.getCurrentTurn();
long move = players[currentTurn].getMove(game.deepCopy());
game.play(move);
IO.println(System.nanoTime() - midtime);
}
switch (game.getWinner()){
case 0:
p1wins++;
break;
case 1:
p2wins++;
break;
case -1:
draws++;
break;
}
}
System.out.println(System.nanoTime() - start);
IO.println(p1wins + " " + p2wins + " " + draws);
assertEquals(totalGames, p1wins + p2wins + draws);
IO.println("p1 wr: " + p1wins + "/" + totalGames + " = " + (double) p1wins / totalGames);
}
}