Reinforcement Learning basic tasks

A Hex board game with a customizable Monte Carlo Tree Search (MCTS) agent with optional leaf parallelization in C++14. Includes a logging functionality for MCTS insights.

Trude's Troops is a short card/auto battler game.

Tic-tac-toe/"noughts & crosses" written in Clojure (CLI + deps). AI powered by Monte Carlo tree search algorithm

AI Pathfinder Game

AI-based Gomoku game bot, focusing on performance and strategic gameplay, competing in tournaments against other optimized bots on piskvork.

We compare different policies for the checkers game using reinforcement learning algorithms.

An extended version of Tic-Tac-Toe, with the option to play against other humans or an AI agent

This repository contains implementations of popular Reinforcement Learning algorithms.

SUSTech CS311 Artificial Intelligence (H, Spring 2024) Project 1

Tic Tac Toe Implementation with Minimax and Monte Carlo Tree Search (MCTS) AI bots using Raylib

A fast-paced turn-based game with several advanced algorithms to verse.

Yet Another "Monte-Carlo Tree Search" implementation

Cranes problem with Monte Carlo Tree Search algorithm

MCTS application : Tic Tac Toe

Monte Carlo Tree Search algorithm applied to a simplified version of Blizzard's Hearthstone. Explores various types of greedy AI agents to learn from and beat down. A part of Reinforcement Learning class at Wrocław University of Science and Technology

Docker files for connecting the PROST planner with pyRDDLGym.

Othello Game created by 2 different AI ALgorithms. These are Minimax and MCT ALgorithms.

Efficient algorithm for making informed decisions in games and other decision-making scenarios. It combines elements of simulation, random sampling, and decision tree analysis to make accurate predictions in real-time. The algorithm is written in Kotlin, a modern and expressive programming language, making it easy to understand and modify.