Reinforcement learning methods in board and MOBA games

Hongyi Chai

doi:10.54254/2755-2721/2/20220556

Research Article

Open access

Published on 22 March 2023

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Chai,H. (2023). Reinforcement learning methods in board and MOBA games. Applied and Computational Engineering,2,105-112.

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Reinforcement learning methods in board and MOBA games

Hongyi Chai *^,1,

¹ University of California, Davis. 1 Shields Ave, Davis, CA 95616

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/2/20220556

Abstract

This article provided an introduction of applying reinforcement learning to games, including board games and video games like Backgammon, Go, and Dota2. The reason for choosing reinforcement learning to solve game problems was analyzed. The article also reviewed the reinforcement learning technique and introduced two optimizing learning methods, Temporal Difference learning and Q learning. Then, three important cases of using reinforcement learning to reach high level game skill, TD-gammon, AlphaGo, and OpenAI Five, were introduced. In the end, the future possibility of applying reinforcement learning in broader way was analyzed.

Keywords

board games, video games, reinforcement learning, Backgammon, Go, Dota2.

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References

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Cite this article

Chai,H. (2023). Reinforcement learning methods in board and MOBA games. Applied and Computational Engineering,2,105-112.

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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)

Conference website: https://www.confcds.org/

ISBN：978-1-915371-19-5(Print) / 978-1-915371-20-1(Online)

Conference date: 16 July 2022

Editor：Alan Wang

Series: Applied and Computational Engineering

Volume number: Vol.2

ISSN：2755-2721(Print) / 2755-273X(Online)

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