AlphaZeroNano

AlphaZeroNano is a miniature implementation of the famous AlphaZero, designed to understand and master grid-based board games. This project offers a lightweight framework for exploring the principles behind deep reinforcement learning combined with Monte Carlo Tree Search (MCTS) in the context of board games.

Directory Structure

The project structure is as follows:

├── .github/workflows     # Contains GitHub workflow definitions (e.g., for linting with pylint)
├── Othello               # Added games from alpha_zero_general repository (MIT License)
├── TicTacToe             # Added games from alpha_zero_general repository (MIT License)
├── tests                 # Directory for test scripts
├── agent.py              # Implementation of AlphaZero agent and necessary functions: training & evaluation
├── main.py               # Main program entry
├── mcts.py               # Monte Carlo Tree Search and supporting functions
├── models.py             # Convolutional neural network for board state evaluation
└── README.md             # Documentation of the project

Requirements

To successfully run and develop the AlphaZeroNano project, you'll need the following libraries:

• torch
• numpy

Acknowledgments

Inspired by the groundbreaking work on AlphaZero by DeepMind, this project serves as an educational tool to understand the underlying principles and mechanisms in a simplified context.