MCTS-Tic-Tac-Toe

Welcome to the MCTS-Tic-Tac-Toe project! This is a web-based application where you can play Tic-Tac-Toe against an AI opponent that uses the Monte Carlo Tree Search (MCTS) algorithm. The AI performs multiple simulations to select the best possible move, giving you a challenging game.

Features

• Play Tic-Tac-Toe interactively through your browser.
• AI opponent powered by Monte Carlo Tree Search.
• Streamlit-based web interface.
• Real-time move evaluation and visualization.

How It Works

The AI uses the Monte Carlo Tree Search (MCTS) algorithm to play Tic-Tac-Toe. It performs the following steps for each move:

1. Simulate: Randomly plays out multiple games from the current game state.
2. Evaluate: Each simulation is evaluated to see which move leads to the best outcome.
3. Accumulate: Scores are accumulated for each possible move.
4. Select: The move with the highest score is chosen as the AI's next move.

Tech Stack

• Python: Game logic and AI implementation.
• Streamlit: For building the web interface.
• Monte Carlo Tree Search: Used for AI decision-making.

Installation

1. Clone the repository:

   git clone https://github.com/PhenomSG/MCTS-Tic-Tac-Toe.git

2. Navigate to the project directory:

   cd MCTS-Tic-Tac-Toe

3. Install the required dependencies:

   pip install streamlit

4. Run the application:

   streamlit run tic_tac_toe_app.py

How to Play

1. After launching the application, you will be presented with a 3x3 Tic-Tac-Toe board.
2. You play as Player O. Click on any empty cell to make your move.
3. The AI, playing as Player X, will make its move after yours.
4. Continue playing until one player wins or the game ends in a draw.

Example Gameplay

Future Improvements

• Adding difficulty levels by adjusting the number of simulations.
• Implementing a larger board or different game variations.
• Adding a more sophisticated evaluation function for MCTS.

License

This project is licensed under the MIT License