Build your own Unbeatable TicTacToe AI
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Learn how to create your own AI TicTacToe player using Reinforcement Learning and the Monte Carlo Method. Train your agent and beat human opponents in this exciting project. Apply machine learning concepts to a well-known game and explore the world of reinforcement learning.
At a Glance
Everyone has played TicTacToe at least once in their life. Have you ever thought about creating your own AI TicTacToe player? Reinforcement Learning can help you with that. In this project, you will explore the basics of Reinforcement Learning and the Monte Carlo Method. You will learn how to train your own agent and create a highly-skilled AI player. This project is an interesting and challenging way to apply machine learning concepts to a well-known game.
Welcome to the exciting world of reinforcement learning! As you may already know, Tic-Tac-Toe is a simple but addictive game played on a 3×3 grid.But what if we could train a machine to play Tic-Tac-Toe against a human opponent, and potentially even beat us at our own game? That’s where reinforcement learning comes in. It was thought it would take decades before a computer beat a Go champion. But now, thanks to reinforcement learning, computers can easily beat Go champions, beat Chess Grandmasters and outperform Humans in every game. In this project, you will use Monte Carlo Reinforcement learning algorithms to train agents for Tic-Tac-Toe . You will quickly grape import concepts of Reinforcement learning and apply open AI’s gym, the go-to framework for Reinforcement learning.
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