Research on Board Game Strategy Methods Based on Reinforcement Learning

Runzhou Luo; Boning Yao; Qingwen Zhang

doi:10.54254/2755-2721/2025.PO24698

Applied and Computational EngineeringOpen access

Research on Board Game Strategy Methods Based on Reinforcement Learning

Research Article

Open Access

Research on Board Game Strategy Methods Based on Reinforcement Learning

Runzhou Luo ^1* Boning Yao ², Qingwen Zhang ³

¹ School of Software, Dalian University of Technology, Dalian 116081, China

² College of Artificial Intelligence, Shenyang Normal University, Shenyang 110034, China

³ International Education College, Shanghai Jian Qiao University, Shanghai 201306, China

^*Corresponding author: 1919493194@mail.dlut.edu.cn

Published on 4 July 2025

ACE Vol.174

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-235-5

ISBN (Online): 978-1-80590-236-2

Download Cover

Abstract

As a typical sequential decision-making and gaming problem, board games have the complexity of large state space and strong dynamic confrontation, however, traditional methods have many limitations in dealing with them, so they need to be based on reinforcement learning to achieve strategy optimization by virtue of data-driven. Reinforcement learning can promote the realization of AI decision-making ability from rule-dependent to data-driven leap, and show significant advantages in game AI. This paper systematically sorts out the core algorithms of reinforcement learning in board games, comparatively analyzes their technical characteristics, applicable scenarios, advantages and disadvantages, discusses the current technical bottlenecks and ethical challenges, and look forward to the future development direction. This paper concludes that reinforcement learning is effective in board games, which not only helps AIs such as AlphaGo and Libratus to surpass the human level in Go, Texas Hold'em and other scenarios, but also forms the transition from “model-dependent” to “data-driven”, From “model-dependent” to “data-driven”, and from “single-intelligence” to “multi-intelligence”, it has also formed a technological evolution vein. At the same time, reinforcement learning has been breaking through in processing high-dimensional states, complex reward functions, etc., and has shown the potential of generalization in the fields of education, healthcare, etc. [1]. This paper can provide theoretical references and practical guidance for subsequent AI research on board games, as well as a universal methodology for complex decision-making problems.

Keywords:

Reinforcement learning, board games, Q-learning, DQN, PPO

View PDF

References

[1]. Ronaldo e Silva Vieira, Anderson Rocha Tavares, Luiz Chaimowicz, Exploring reinforcement learning approaches for drafting in collectible card games, Entertainment Computing, Volume 44, 2023, 100526, ISSN 1875-9521, https: //doi.org/10.1016/j.entcom.2022.100526.

[2]. Zha D , Xie J , Ma W , et al.DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning [J]. 2021.DOI: 10.48550/arXiv.2106.06135.

[3]. Kong, Y., Shi, H., Wu, X. et al. Application of DQN-IRL Framework in Doudizhu’s Sparse Reward. Neural Process Lett 55, 9467–9482 (2023). https: //doi.org/10.1007/s11063-023-11209-0

[4]. Luo and T. -P. Tan, "RARSMSDou: Master the Game of DouDiZhu With Deep Reinforcement Learning Algorithms, " in IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 8, no. 1, pp. 427-439, Feb. 2024, doi: 10.1109/TETCI.2023.3303251.

[5]. Heinrich J , Silver D .Deep Reinforcement Learning from Self-Play in Imperfect-Information Games [J]. 2016.DOI: 10.48550/arXiv.1603.01121.

[6]. J. M and R. Surendran, "Chess Game to Improve the Mental Ability of Alzheimer's Patients using A3C, " 2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT), Erode, India, 2023, pp. 1-6, doi: 10.1109/ICECCT56650.2023.10179809.

[7]. Zhechao Wang, Qiming Fu, Jianping Chen, Quan Liu, You Lu, Hongjie Wu, Fuyuan Hu, LinFa-Q: Accurate Q-learning with linear function approximation, Neurocomputing, Volume 611, 2025, 128654, ISSN 0925-2312, https: //doi.org/10.1016/j.neucom.2024.128654.

[8]. Wang Z , Fu Q , Lu Y , et al.LinFa-Q: Accurate Q-learning with linear function approximation [J].Neurocomputing, 2025(Jan.1): 611.DOI: 10.1016/j.neucom.2024.128654.

[9]. Clark T , Towers M , Evers C , et al.Beyond The Rainbow: High Performance Deep Reinforcement Learning On A Desktop PC [J]. 2024.

[10]. Wu L , Wu Q , Zhong H , et al.Mastering "Gongzhu" with Self-play Deep Reinforcement Learning [C]//International Conference on Cognitive Systems and Signal Processing.Springer, Singapore, 2023.DOI: 10.1007/978-981-99-0617-8_11.

[11]. Liu Z .Learning Explainable Policy For Playing Blackjack Using Deep Reinforcement Learning (Reinforcement Learning) [J]. 2021.

References

[2]. Zha D , Xie J , Ma W , et al.DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning [J]. 2021.DOI: 10.48550/arXiv.2106.06135.

[3]. Kong, Y., Shi, H., Wu, X. et al. Application of DQN-IRL Framework in Doudizhu’s Sparse Reward. Neural Process Lett 55, 9467–9482 (2023). https: //doi.org/10.1007/s11063-023-11209-0

[5]. Heinrich J , Silver D .Deep Reinforcement Learning from Self-Play in Imperfect-Information Games [J]. 2016.DOI: 10.48550/arXiv.1603.01121.

[8]. Wang Z , Fu Q , Lu Y , et al.LinFa-Q: Accurate Q-learning with linear function approximation [J].Neurocomputing, 2025(Jan.1): 611.DOI: 10.1016/j.neucom.2024.128654.

[9]. Clark T , Towers M , Evers C , et al.Beyond The Rainbow: High Performance Deep Reinforcement Learning On A Desktop PC [J]. 2024.

[11]. Liu Z .Learning Explainable Policy For Playing Blackjack Using Deep Reinforcement Learning (Reinforcement Learning) [J]. 2021.

Cite this article

Luo,R.;Yao,B.;Zhang,Q. (2025). Research on Board Game Strategy Methods Based on Reinforcement Learning. Applied and Computational Engineering,174,32-38.

Data availability

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

About volume

Volume title: Proceedings of CONF-CDS 2025 Symposium: Data Visualization Methods for Evaluatio

ISBN: 978-1-80590-235-5(Print) / 978-1-80590-236-2(Online)

Editor: Marwan Omar, Elisavet Andrikopoulou

Conference website: https://2025.confcds.org/portsmouth.html

Conference date: 30 July 2025

Series: Applied and Computational Engineering

Volume number: Vol.174

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

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:

1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.

2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.

3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).