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Advances in Computer Games

ACG 2021: Advances in Computer Games pp 27–37Cite as

Expert Iteration for Risk

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13262))

Abstract

Risk is a complex strategy game that may be easier to understand for humans than chess but harder to deal with for computers. The main reasons are the stochastic nature of battles and the different decisions that must be coordinated within turns. Our goal is to create an artificial intelligence able to play the game without human knowledge using the Expert Iteration [1] framework. We use graph neural networks [13, 15, 22, 30] to learn the policies for the different decisions and the value estimation. Experiments on a synthetic board show that with this framework the model can rapidly learn a good country drafting policy, while the main game phases remain a challenge.

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Notes

  1. 1.

    https://github.com/lucasgneccoh/pyLux.

  2. 2.

    https://sillysoft.net/lux/.

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Acknowledgment

This work was supported in part by the French government under management of Agence Nationale de la Recherche as part of the “Investissements d’avenir" program, reference ANR19-P3IA-0001 (PRAIRIE 3IA Institute).

Author information

Authors and Affiliations

  1. LAMSADE, Université Paris-Dauphine, PSL, CNRS, Paris, France

    Lucas Gnecco Heredia & Tristan Cazenave

Authors
  1. Lucas Gnecco Heredia
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  2. Tristan Cazenave
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Correspondence to Tristan Cazenave .

Editor information

Editors and Affiliations

  1. Maastricht University, Maastricht, The Netherlands

    Cameron Browne

  2. IBM Research - Tokyo, Tokyo, Japan

    Akihiro Kishimoto

  3. University of Alberta, Edmonton, AB, Canada

    Jonathan Schaeffer

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Heredia, L.G., Cazenave, T. (2022). Expert Iteration for Risk. In: Browne, C., Kishimoto, A., Schaeffer, J. (eds) Advances in Computer Games. ACG 2021. Lecture Notes in Computer Science, vol 13262. Springer, Cham. https://doi.org/10.1007/978-3-031-11488-5_3

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  • DOI: https://doi.org/10.1007/978-3-031-11488-5_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-11487-8

  • Online ISBN: 978-3-031-11488-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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