KrwEmd: Revising the Imperfect-Recall Abstraction from Forgetting Everything

TL;DR

KrwEmd is a novel algorithm that improves imperfect-recall abstraction in large-scale poker AI by clustering game states using earth mover's distance, leveraging both historical and future information.

Contribution

It introduces the k-recall winrate feature and the KrwEmd algorithm, which effectively revise imperfect-recall abstraction by considering historical data, enhancing AI performance in complex games.

Findings

KrwEmd outperforms existing algorithms in AI gameplay.

The k-recall winrate feature effectively captures signal similarities.

Clustering with earth mover's distance improves state abstraction.

Abstract

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.