基于AlphaZero的WallGo智能体，击败专业棋手并揭示游戏策略
WallZero: Mastering the Game of WallGo with Strategic Analysis
- 采用定制动作与特征设计，提升智能体对墙与石子移动的策略理解
- 平均每局比专业棋手多占1.98倍领地，表现显著更强
- 分析发现剧集中的开局更公平，适合想掌握游戏核心策略者
WallGo 是一种近期由 Netflix 系列《恶魔计划》（The Devil's Plan, 2025）推广的双人策略棋类游戏。尽管棋盘仅7×7，但棋子移动与墙放置的结合带来了高复杂度的游戏树和复杂的策略互动。尽管日益流行，其研究仍有限。本文提出 WallZero，一个基于 AlphaZero 的双人 WallGo 智能体。通过引入定制化的动作空间与状态特征表示，显著提升对弈表现。评估显示，WallZero 在与两位参与本研究的专业围棋选手对弈中，平均每局多获得1.98倍领地。此外，利用 WallZero 分析游戏公平性并识别关键制胜策略。结果表明，《恶魔计划》中使用的开局更具平衡性。代码已公开于 https://rlg.iis.sinica.edu.tw/papers/wallzero。
原文摘要 · Abstract (English)
WallGo is a recently introduced strategic board game popularized by the 2025 Netflix series The Devil's Plan. Although played on a small 7 x 7 board, its combination of stone movement and wall placement yields high game-tree complexity and intricate strategic interactions. Despite its growing popularity, WallGo remains underexplored. This paper presents WallZero, an AlphaZero-based agent for the two-player WallGo setting. We introduce tailored action and feature designs to improve playing performance significantly. In the evaluation, WallZero defeats two professional Go players who participated in this study, securing on average 1.98x more territory per game. Beyond its strength, we use WallZero to assess game fairness and identify key strategies for mastering WallGo. Interestingly, our results show that the opening used in the Netflix series yields a more balanced game. Our code is available at https://rlg.iis.sinica.edu.tw/papers/wallzero.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。