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From Shogi and Chess to Reinforcement Learning: A Study of NNUEs in More General Settings

In: Operations Research Proceedings 2023

Author

Listed:
  • Philipp Triebold

    (Hochschule Furtwangen University
    Universität der Bundeswehr München)

  • Maximilian Moll

    (Universität der Bundeswehr München)

  • Hans-Georg Enkler

    (Hochschule Furtwangen University)

  • Stefan Pickl

    (Universität der Bundeswehr München)

Abstract

The continued development of evaluation functions for use in chess and shogi engines resulted in the development of Efficiently Updatable Neural Networks in 2018 by Yu Nasu. These utilise the full potential of modern processors foregoing the need for specialised hardware and thus decreasing cost and energy consumption. There are three central optimisations, leveraging the sparsity and redundancy in the encoding, lowering the bit width and pivoting all calculations to integers, and lastly using advanced vectorisation with single instruction multiple data registers. These optimisations are evaluated for their contribution to Efficiently Updatable Neural Networks and how they could impact efficiency and speed in different environments. Finally, the optimisations are implemented in Python and C++ to test their real-world benefits.

Suggested Citation

  • Philipp Triebold & Maximilian Moll & Hans-Georg Enkler & Stefan Pickl, 2025. "From Shogi and Chess to Reinforcement Learning: A Study of NNUEs in More General Settings," Lecture Notes in Operations Research, in: Guido Voigt & Malte Fliedner & Knut Haase & Wolfgang Brüggemann & Kai Hoberg & Joern Meissner (ed.), Operations Research Proceedings 2023, chapter 0, pages 567-572, Springer.
    • Handle: RePEc:spr:lnopch:978-3-031-58405-3_72
    DOI: 10.1007/978-3-031-58405-3_72
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