MCTS for Tic Tac Toe

Title: MCTS for Tic Tac Toe

Channel Name: detroyist

Description: Game of the tic tac toe with an IA to play with. This code was made just for fun and to give a start point for future implementations of search tree algorithms. The game has a graphic interface and a terminal one, both can be played with different board sizes (3x3, 4x4..). The code is done in Python 3.7 and the actual IA implemented is Monte Carlo Tree Search, a heuristic algorithm used in AlphaGo. Hope you enjoy it and if you are new in Artificial Intelligence like me, hope this is helpful. GIT: https://github.com/ipmach/TicTacToe_IA MUSIC: Tesler's Party - Joseph Trapanese

Summary Overview: The video showcases a Python implementation of a Tic Tac Toe game with an AI opponent using the Monte Carlo Tree Search algorithm. The game offers both a graphic interface and a terminal interface with the flexibility to play on different board sizes. The tutorial aims to provide a fun introduction to AI concepts and programming in Python.

Tutorial Instructions:

1. Introduction to the Tic Tac Toe Game:

   • Understand the basics of the Tic Tac Toe game where two players take turns marking X and O on a 3x3 grid to achieve a winning pattern.

2. Setting Up the Environment:

   • Ensure you have Python 3.7 installed on your system to run the code.
   • Visit the GitHub repository at https://github.com/ipmach/TicTacToe_IA to access the code.

3. Implementing Monte Carlo Tree Search Algorithm:

   • Study the Monte Carlo Tree Search algorithm used in the AI opponent for the game.
   • Learn how the algorithm simulates multiple random games to determine the best move.

4. Running the Game:

   • Clone or download the repository to your local machine.
   • Open the Python script in your preferred editor to explore the code.
   • Run the script to start the Tic Tac Toe game with the AI opponent.

5. Playing the Game:

   • Follow the on-screen instructions to make your moves against the AI.
   • Experiment with different board sizes such as 3x3, 4x4, etc., to vary the complexity of the game.

6. Exploring the Graphic Interface:

   • Interact with the graphic interface to visually play the game and experience a user-friendly interface.

7. Learning Opportunities:

   • Dive deeper into the code to understand how the AI opponent makes its decisions using the Monte Carlo Tree Search algorithm.
   • Experiment with modifying the code to implement additional AI algorithms or enhance the game features.

8. Enjoying the Experience:

   • Have fun playing Tic Tac Toe against the AI opponent and appreciate the learning opportunity provided by the tutorial.

9. Further Resources:

   • Explore more AI concepts, algorithms, and Python programming techniques to expand your knowledge in Artificial Intelligence.

By following these steps, you can engage with the MCTS for Tic Tac Toe tutorial presented in the video and enhance your understanding of AI concepts through practical implementation in Python.