Research on Board Game Strategy Methods Based on Reinforcement Learning Article Swipe

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YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.54254/2755-2721/2025.po24698

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.

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Reinforcement Learning

Computer Science

Artificial Intelligence

Human–Computer Interaction

Social Psychology

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Reinforcement Reinforcement learning Computer science Psychology Artificial intelligence Human–computer interaction Social psychology

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Type: article
Language: en
Landing Page: https://doi.org/10.54254/2755-2721/2025.po24698
PDF: https://www.ewadirect.com/proceedings/ace/article/view/24698/pdf
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Related Works: 10
OpenAlex ID: https://openalex.org/W4411989149

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DOI: https://doi.org/10.54254/2755-2721/2025.po24698

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Title: Research on Board Game Strategy Methods Based on Reinforcement Learning

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Type: article

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Language: en

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Publication year: 2025

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Publication date: 2025-07-03

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Authors: Rong Luo, Bing Yao, Qingwen Zhang

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Landing page: https://doi.org/10.54254/2755-2721/2025.po24698

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PDF URL: https://www.ewadirect.com/proceedings/ace/article/view/24698/pdf

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OA URL: https://www.ewadirect.com/proceedings/ace/article/view/24698/pdf

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Concepts: Reinforcement, Reinforcement learning, Computer science, Psychology, Artificial intelligence, Human–computer interaction, Social psychology

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