The Explainable Chess Engine - Introduction

Explainable Chess Engine

A minimalist DL chess engine with explainability.

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About The Project

This project aim to build a minimalist Deep Learning chess engine with explainability. The goal is to create a chess engine that can explain its decisions in a human-readable way. This project will be a great way to learn about chess engines, deep learning, and explainability in AI.

Objectives

• Build a Deep Learning framework optimized for training with chess data.
• Monitor the performance of the training framework to optimize training time, CPU/GPU usage, and memory usage.
• Demonstrate proficiency in PyTorch, Tensorflow, Keras and Jax.
• Build the best model with the minimal number of parameters for several ELO levels.
• Create a human-readable explanation of the model’s decisions.
• Implement a chess engine that can play against human or AI players using the model.

Roadmap

• Create the project structure
• Pytorch Framework
  • Custom pytorch dataset for chess data
    • Read any board state at any game number in a PGN file
    • Convert board, moves and game result to tensor
    • Batch all operations
    • Tests
  • Training loop & utilities for training
    • Training loop
    • Logging & Tensorboard
    • Evaluation
    • Save & Load model
    • Tests for training framework
• Tensorflow Framework
  • Custom TFRecords for chess data
    • Read any board state at any game number in a PGN file
    • Convert board, moves and game result to tensor
    • Batch all operations
    • Tests
• Chess Engine
  • Beam Search using the DL models
  • Implement matches
  • Evaluate against Stockfish
  • Tests for the chess engine
• Explainability
  • Beam Search visualization
  • Board evaluation visualization
• Deployment
  • Dockerize the project
  • Deploy on Lichess
  • Deploy on HuggingFace

Demo