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