This repository contains links to the algorithmic game solver repositories I have worked on, which implement apps that use techniques and algorithms like mathematically proven solutions, minimax with alpha-beta pruning, Monte Carlo tree simulations, and neural networks to accomplish a strong performance on games such as Rock-Paper Scissors, Blackjack, Othello (Reversi), and more:
These solvers use specialized, mathematically proven formulas to guarantee success in any situation to which a solution can be found:
These solvers use the minimax algorithm to determine the best move at each step of the game, with the solution having a mathematical guarantee of correctness for some games like 3x3 Tic-Tac-Toe. Alpha-beta pruning is used to ensure the efficiency of this algorithm:
- 3D Tic-Tac-Toe
- 3 Men's Morris
- 6 Men's Morris
- 9 Men's Morris/Mills
- Colonel Blotto
- Connect Four
- Dots and Boxes
- Othello
- Oware
- Tic-Tac-Toe
These solvers use a Monte Carlo tree simulation to make an educated guess after sampling dozens of observations:
This solver has the user train a neural network to learn how to play the game: