Monte Carlo Tree Search: Implementation for Tic-Tac-Toe

Question

Edit: Uploded the full source code if you want to see if you can get the AI to perform better: https://www.dropbox.com/s/ous72hidygbnqv6/MCTS_TTT.rar

Edit: The search space is searched and moves resulting in losses are found. But moves resulting in losses are not visited very often due to the UCT algorithm.

To learn about MCTS (Monte Carlo Tree Search) I've used the algorithm to make an AI for the classic game of tic-tac-toe. I have implemented the algorithm using the following design:

The tree policy is based on UCT and the default policy is to perform random moves until the game ends. What I have observed with my implementation is that the computer sometimes makes errorneous moves because it fails to "see" that a particular move will result in a loss directly.

For instance: Notice how the action 6 (red square) is valued slightly higher than the blue square and therefore the computer marks this spot. I think this is because the game policy is based on random moves and therefore a good chance exist that the human will not put a "2" in the blue box. And if the player does not put a 2 in the blue box, the computer is gaurenteed a win.

My Questions

1) Is this a known issue with MCTS or is it a result of a failed implementation?

2) What could be possible solutions? I'm thinking about confining the moves in the selection phase but I'm not sure :-)

The code for the core MCTS:

    //THE EXECUTING FUNCTION
    public unsafe byte GetBestMove(Game game, int player, TreeView tv)
    {

        //Setup root and initial variables
        Node root = new Node(null, 0, Opponent(player));
        int startPlayer = player;

        helper.CopyBytes(root.state, game.board);

        //four phases: descent, roll-out, update and growth done iteratively X times
        //-----------------------------------------------------------------------------------------------------
        for (int iteration = 0; iteration < 1000; iteration++)
        {
            Node current = Selection(root, game);
            int value = Rollout(current, game, startPlayer);
            Update(current, value);
        }

        //Restore game state and return move with highest value
        helper.CopyBytes(game.board, root.state);

        //Draw tree
        DrawTree(tv, root);

        //return root.children.Aggregate((i1, i2) => i1.visits > i2.visits ? i1 : i2).action;
        return BestChildUCB(root, 0).action;
    }

    //#1. Select a node if 1: we have more valid feasible moves or 2: it is terminal 
    public Node Selection(Node current, Game game)
    {
        while (!game.IsTerminal(current.state))
        {
            List<byte> validMoves = game.GetValidMoves(current.state);

            if (validMoves.Count > current.children.Count)
                return Expand(current, game);
            else
                current = BestChildUCB(current, 1.44);
        }

        return current;
    }

    //#1. Helper
    public Node BestChildUCB(Node current, double C)
    {
        Node bestChild = null;
        double best = double.NegativeInfinity;

        foreach (Node child in current.children)
        {
            double UCB1 = ((double)child.value / (double)child.visits) + C * Math.Sqrt((2.0 * Math.Log((double)current.visits)) / (double)child.visits);

            if (UCB1 > best)
            {
                bestChild = child;
                best = UCB1;
            }
        }

        return bestChild;
    }

    //#2. Expand a node by creating a new move and returning the node
    public Node Expand(Node current, Game game)
    {
        //Copy current state to the game
        helper.CopyBytes(game.board, current.state);

        List<byte> validMoves = game.GetValidMoves(current.state);

        for (int i = 0; i < validMoves.Count; i++)
        {
            //We already have evaluated this move
            if (current.children.Exists(a => a.action == validMoves[i]))
                continue;

            int playerActing = Opponent(current.PlayerTookAction);

            Node node = new Node(current, validMoves[i], playerActing);
            current.children.Add(node);

            //Do the move in the game and save it to the child node
            game.Mark(playerActing, validMoves[i]);
            helper.CopyBytes(node.state, game.board);

            //Return to the previous game state
            helper.CopyBytes(game.board, current.state);

            return node;
        }

        throw new Exception("Error");
    }

    //#3. Roll-out. Simulate a game with a given policy and return the value
    public int Rollout(Node current, Game game, int startPlayer)
    {
        Random r = new Random(1337);
        helper.CopyBytes(game.board, current.state);
        int player = Opponent(current.PlayerTookAction);

        //Do the policy until a winner is found for the first (change?) node added
        while (game.GetWinner() == 0)
        {
            //Random
            List<byte> moves = game.GetValidMoves();
            byte move = moves[r.Next(0, moves.Count)];
            game.Mark(player, move);
            player = Opponent(player);
        }

        if (game.GetWinner() == startPlayer)
            return 1;

        return 0;
    }

    //#4. Update
    public unsafe void Update(Node current, int value)
    {
        do
        {
            current.visits++;
            current.value += value;
            current = current.parent;
        }
        while (current != null);
    }

Answer 1

I think your answer shouldn't be marked as accepted. For Tic-Tac-Toe the search space is relatively small and optimal action should be found within a reasonable number of iterations.

It looks like your update function (backpropagation) adds the same amount of reward to nodes at different tree levels. This is not correct, since states current players are different at different tree levels.

I suggest you take a look at backpropagation in the UCT method from this example: http://mcts.ai/code/python.html

You should update node's total reward based on the reward calculated by previous player at specific level (node.playerJustMoved in the example).