This library implements a simple Monte Carlo Tree Search for games.
The datasets generated throughout the dissertation are available here.
To use a dataset, install couchbase, restore the database from any folder and boot the botwars framework. All game states are fully queriable through a N1QL query editor and also accessible in the web interface.
Implement a class that has the following methods:
- getPossibleMoves() returns an array of possible moves
- performMove(move) updates the internal state of the game based on the move
- getCurrentPlayer() returns the current player
- getWinner() returns the winner or undefined if there is no winner
Then to get the next move a player should perform call getcall MCTS with an instance of your game class. Here is a totally contrived game where whoever goes first wins:
MCTS = require('mcts').MCTS;
function Game() {
this.winner = null;
}
Game.prototype.getPossibleMoves = function () {
if (this.winner === null) {
return [0];
}
return [];
};
Game.prototype.getCurrentPlayer = function () {
return 0;
};
Game.prototype.performMove = function (player) {
this.winner = player;
};
Game.prototype.getWinner = function () {
return this.winner;
};
var mcts = new MCTS(new Game());
console.log(mcts.selectMove());
0