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Reinforcement Learning in Games

  • Chapter
Reinforcement Learning

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 12))

Abstract

Reinforcement learning and games have a long and mutually beneficial common history. From one side, games are rich and challenging domains for testing reinforcement learning algorithms. From the other side, in several games the best computer players use reinforcement learning. The chapter begins with a selection of games and notable reinforcement learning implementations.Without any modifications, the basic reinforcement learning algorithms are rarely sufficient for high-level gameplay, so it is essential to discuss the additional ideas, ways of inserting domain knowledge, implementation decisions that are necessary for scaling up. These are reviewed in sufficient detail to understand their potentials and their limitations. The second part of the chapter lists challenges for reinforcement learning in games, together with a review of proposed solution methods. While this listing has a game-centric viewpoint, and some of the items are specific to games (like opponent modelling), a large portion of this overview can provide insight for other kinds of applications, too. In the third part we review how reinforcement learning can be useful in game development and find its way into commercial computer games. Finally, we provide pointers for more in-depth reviews of specific games and solution approaches.

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  1. University of Alberta, Alberta, Canada

    István Szita

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  1. Fac. Mathematics &, Natural Sciences, University of Groningen, Nijenborgh 9, Groningen, 9747 AG, Netherlands

    Marco Wiering

  2. , Artificial Intelligence, Radboud University Nijmegen, B.02.30 Spinozagebouw, Montessorilaan 3, Nijmegen, 6500, Netherlands

    Martijn van Otterlo

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Szita, I. (2012). Reinforcement Learning in Games. In: Wiering, M., van Otterlo, M. (eds) Reinforcement Learning. Adaptation, Learning, and Optimization, vol 12. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27645-3_17

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