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19 April 2024 |
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Article overview
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Neural Networks for Chess | | Dominik Klein
; | | Date: |
4 Sep 2022 | | Abstract: | AlphaZero, Leela Chess Zero and Stockfish NNUE revolutionized Computer Chess.
This book gives a complete introduction into the technical inner workings of
such engines. The book is split into four main chapters -- excluding chapter 1
(introduction) and chapter 6 (conclusion): Chapter 2 introduces neural networks
and covers all the basic building blocks that are used to build deep networks
such as those used by AlphaZero. Contents include the perceptron,
back-propagation and gradient descent, classification, regression, multilayer
perceptron, vectorization techniques, convolutional networks, squeeze and
excitation networks, fully connected networks, batch normalization and
rectified linear units, residual layers, overfitting and underfitting. Chapter
3 introduces classical search techniques used for chess engines as well as
those used by AlphaZero. Contents include minimax, alpha-beta search, and Monte
Carlo tree search. Chapter 4 shows how modern chess engines are designed. Aside
from the ground-breaking AlphaGo, AlphaGo Zero and AlphaZero we cover Leela
Chess Zero, Fat Fritz, Fat Fritz 2 and Efficiently Updatable Neural Networks
(NNUE) as well as Maia. Chapter 5 is about implementing a miniaturized
AlphaZero. Hexapawn, a minimalistic version of chess, is used as an example for
that. Hexapawn is solved by minimax search and training positions for
supervised learning are generated. Then as a comparison, an AlphaZero-like
training loop is implemented where training is done via self-play combined with
reinforcement learning. Finally, AlphaZero-like training and supervised
training are compared. | | Source: | arXiv, 2209.01506 | | Services: | Forum | Review | PDF | Favorites | |
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