Monte-Carlo-Tree Search not working

Question

I am currently writing an AI for the board game Hex. I want to use Monte-Carlo-Tree-Search to do so and have already tried to implement it. However, the AI makes incredible stupid (random) moves and I can not figure out why it`s not working.

import java.util.ArrayList;
import java.util.Random;

/**
 * Created by Robin on 18.03.2017.
 */
public class TreeNode {


    private static final Random random = new Random();
    private static final double epsion=10e-5;
    protected double nvisits;
    protected double totValue;
    protected int move=-1;

    private HexBoard board;
    protected ArrayList<TreeNode>children ;



    public TreeNode(HexBoard board){
        this.board =board;
    }


    //Copy-Constructor
    public TreeNode(TreeNode treeNode){
        this.nvisits=treeNode.nvisits;
        this.totValue=treeNode.totValue;
        this.move=treeNode.move;
        this.board = new HexBoard(treeNode.board);

    }

    public void update(double value){
        totValue+=value*board.color;
        nvisits++;
    }



    public void expand(){
        assert(children==null);
        children = new ArrayList<>(121-board.moveCount);
        for(int i=0;i<121;i++){
            if(board.board[i]!=HexBoard.EMPTY)
                continue;

                TreeNode newNode = new TreeNode(board);
                newNode.move =i;
                children.add(newNode);

        }
    }

    public void calculateIteration(){
        ArrayList<TreeNode>visited = new ArrayList<>();
        TreeNode current =this;
        visited.add(current);

        while(!current.isLeafNode()){
            current =current.select();
            board.makeMove(current.move);
            visited.add(current);
        }

        //Found a leaf node
        double value;
        if(current.board.getWinner()==0){
            current.expand();
            TreeNode newNode =current.select();
            value =playOut(newNode.board);
        }else{
            value =current.board.getWinner();
        }

        //update all the nodes

        for(int i=1;i<visited.size();i++){
            visited.get(i).update(value);
            board.undoMove(visited.get(i).move);
        }
        visited.get(0).update(value);
    }

    public static int playOut(HexBoard board){
        int winner=0;

        if(board.moveCount==121) {
            winner=board.getWinner();

            return winner;
        }

        //Checking-Movecount vs actual stones on the board


        final double left =121-board.moveCount;
        double probibility =1/left;
        double summe =0;
        double p =random.nextDouble();

        int randomMove =0;
        for(int i=0;i<121;i++){
            if(board.board[i]!=HexBoard.EMPTY)
                continue;

            summe+=probibility;

            if(p<=summe && probibility!=0) {
                randomMove = i;
                break;
            }
        }

        board.makeMove(randomMove);
        winner =playOut(board);
        board.undoMove(randomMove);

        return winner;
    }


    public TreeNode select(){

        TreeNode bestNode=null;
        double bestValue =-10000000;
        for(TreeNode node : children){

            double uctvalue =(node.nvisits==0)?100000:(node.totValue/(node.nvisits)+Math.sqrt((Math.log(this.nvisits))/(2*node.nvisits)));
            uctvalue+=epsion*random.nextDouble();

            if(uctvalue>bestValue){
                bestValue=uctvalue;
                bestNode =node;
            }
        }

        return bestNode;
        ///
    }

    public boolean isLeafNode(){
        return (children==null);
    }
}

Is my implementation inside the method calcualteIteration() correct ?

I know this might not be a very attractive problem to look at but I would appreciate any help

Answer 1

OP added extra information in comments after the question. The important part of that extra information is that the makeMove() method was implemented to check which player is to play next (to make sure updates to board are correct).

Given that information, the implementation of select() in the OP is not correct, because it does not take into account which player is to move when computing the UCT score. The UCT score consists of an "exploitation" part (the first fraction, computing average score over all previous simulations), and an "exploration" part (the part under square root, which increases for nodes that have been visited rarely relative to their parent). The exploitation part of this equation should be negated when the opponent is allowed to make a move next. If this is not done, the AI will essentially assume that the opponent is willing to actively help the AI, instead of assuming that the opponent will try to win for himself.