Minishogi for CodinGame

Dice Shogi for CodinGame

In this project, my primary goal was to implement an AI player class powered by the Monte Carlo Tress Search algorithm which can play for a win as well as defend a defeat to compete with a Human player.

This projects seeks to explore the performance and dynamics of agents playing in a full Java implemetation of the game Imperial (http://bit.ly/Imperial-wiki). An array of different agent architectures are used ranging from simple rule-based to MCTS-Deep-Neural-Network agents.

Java based alpha zero reinforcement learning. The generic base module allows implementation of any adversary board game. Example implementation for Tic Tac Toe.

MCTS for query rewrite

Tic-Tac-Toe game using the Monte Carlo Tree Search algorithm, implemented in Java.

Cranes problem with Monte Carlo Tree Search algorithm

Abstract Strategy Board Games

This is the AI we created for a university course. It plays the famous game, Kalah.

Text based classic Rummy game with an AI that uses ISMCTS. Data Structures and Algorithms course project, University of Helsinki

Chinese Checkers computer player implementing Monte Carlo Tree Search

Ultimate Tic Tac Toe for Android

Project 2-1 of DKE

Reversi monte carlo tree search for android

MCTS/minimax turn-based game AI for Java

This repository contains the AI engine for a simplified version of Heartstone game

Tetris AI on botzone.org