MCTS combined with neural networks, which learn how to play the game of Connect 4
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Updated
Apr 18, 2018 - Python
MCTS combined with neural networks, which learn how to play the game of Connect 4
Ultimate Tic Tac Toe for Android
Unbeatable AI for the tic tac toe.
An implementation of MinMax and Monte Carlo Tree Search solvers for Connect 4.
A generalized multithreaded artificial intelligence capable of playing: Connect Four, Hex (7 x 7), Reversi (Othello), and Tic-Tac-Toe.
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.
GenesisZERO : potential applications for MCTS agents with LLMs for Sequential decision-making
This repository contains a Python implementation of the classic game Tic Tac Toe with AI opponent. The game is played on a 3x3 grid by two players, one using 'X' and the other using 'O'. The player who first gets 3 of their marks in a row (up, down, across, or diagonally) is the winner.
Monte Carlo Tree Search applied to Combinatorial Search Problems
Monte Carlo Tree Search engine for 8x8 American/English draughts.
Code for the paper "Learning Programs with arguments and sampling"
I bet this bot can consistently beat even you (a human) at Connect-4! It uses the Monte-Carlo Tree search Reinforcement Technique to play
Various AIs for the board game hex, including Monte Carlo Tree Search with the Tsetlin Machine
Implementation of an AI agent for Tic Tac Toe and other games.
Labs and exercises for the course Computational Intelligence.
An implementation of the game Bamboo with several AI agents to play against
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