提出新算法KrwEmd，用历史信息提升德州扑克抽象精度
KrwEmd: Revising the Imperfect-Recall Abstraction from Forgetting Everything
- 引入k-回忆胜率特征，融合历史与未来信息
- 用地球移动距离聚类信息集，显著提升性能
- 适合研究扑克博弈与抽象方法的开发者
在大规模不完美信息博弈（如德州扑克）中，过度抽象会严重损害AI表现，根源在于极端的非回忆抽象完全丢弃历史信息。本文提出首个实用算法KrwEmd，首先引入k-回忆胜率特征，通过结合未来与历史游戏信息，定性区分信号观测信息集，并定量刻画其相似性。随后设计KrwEmd算法，利用地球移动距离（Earth Mover's Distance）度量信息集特征差异并进行聚类。实验表明，相较于现有方法，KrwEmd显著提升了AI对弈表现。
原文摘要 · Abstract (English)
Excessive abstraction is a critical challenge in hand abstraction-a task specific to games like Texas hold'em-when solving large-scale imperfect-information games, as it impairs AI performance. This issue arises from extreme implementations of imperfect-recall abstraction, which entirely discard historical information. This paper presents KrwEmd, the first practical algorithm designed to address this problem. We first introduce the k-recall winrate feature, which not only qualitatively distinguishes signal observation infosets by leveraging both future and, crucially, historical game information, but also quantitatively captures their similarity. We then develop the KrwEmd algorithm, which clusters signal observation infosets using earth mover's distance to measure discrepancies between their features. Experimental results demonstrate that KrwEmd significantly improves AI gameplay performance compared to existing algorithms.
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