Coral

A PyTorch-based neural network library for board game evaluation.

Overview

Coral provides a flexible framework for building and deploying neural networks to evaluate board game positions. It includes modular components for input conversion, neural network architectures, and output interpretation.

Features

• Multiple NN Architectures: Multi-layer perceptrons, transformers, and custom models
• Flexible Input/Output Conversion: Modular converters for different board representations and evaluation formats
• Point-of-View Evaluation: Support for evaluating positions from different player perspectives
• PyTorch Integration: Built on PyTorch with JIT compilation support for optimized inference

Installation

pip install git+https://github.com/victorgabillon/coral.git@main

Requirements

• Python >= 3.13
• PyTorch
• valanga (board game library)

Project Structure

src/coral/
├── board_evaluation.py          # Point-of-view and evaluation types
├── chi_nn.py                     # Base neural network class
└── neural_networks/
    ├── factory.py                # NN factory pattern
    ├── nn_content_evaluator.py   # Board evaluation with NNs
    ├── models/                   # NN architectures (MLP, Transformer)
    ├── input_converters/         # Board to tensor conversion
    └── output_converters/        # NN output to evaluation conversion

License

GPL-3.0-only