Connect4 reinforcement learning by AlphaGo Zero methods.
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Updated
Apr 5, 2021 - Python
Connect4 reinforcement learning by AlphaGo Zero methods.
AlphaZero implementation for Othello, Connect-Four and Tic-Tac-Toe based on "Mastering the game of Go without human knowledge" and "Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm" by DeepMind.
board games for your terminal!
Master Thesis project that provides a training framework for two player games. TicTacToe and Othello have already been implemented.
Visualisation of MCTS in Unity with C# for different games, being created for my third year university project at the University of York
Four-In-A-Row in Flutter
HybridAlpha - a mix between AlphaGo Zero and AlphaZero for multiple games
A browser-based Connect 4 game made with Phaser 3! https://iammanish17.github.io/Connect4/
Connect 4 Game implemented using minimax alpha beta pruning algorithm
AI for the Connect 4 game
A multi-agent implementation of the game Connect-4 using MCTS, Minimax and Exptimax algorithms.
Connect 4 AI using Monte Carlo Tree Search algorithm.
Connect 4 game with online multiplayer
University project in the course ICT Seminar 3. Using Tsetlin Machine for Pattern Recognition in the game Connect Four.
Basic AI engine written in C# that implements the alpha–beta pruning algorithm to create two-player, zero-sum games like Tic Tac Toe, Connect 4, or Chess.
Connect 4 is a token game created with Python and module Numpy. Place 4 tokens in a row (vertically, horizontally, or diagonally) before your opponent wins. Single-player against Artificial Intelligence and multiplayer also available.
AI for the game "Connect Four". Available on PyPI.
Connect-4 AI inspired by the AlphaZero paper that uses Monte-Carlo Tree Search, and a neural policy and value estimator neural network trained with samples generated from self play between previous iterations of the model.
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