Effective, Efficient, and General Information Abstraction for Imperfect-Information Extensive-Form Games

2026-05-11

AI Summary

This work addresses the computational bottleneck in imperfect-information extensive-form games caused by prohibitively large information sets. While existing abstraction techniques often rely on domain-specific knowledge or extensive offline training, the proposed WEVA method requires only a minimal number of Counterfactual Regret Minimization (CFR) warm-up iterations—such as ten—to extract expected-value features and construct deep weighted multi-node feature vectors. These vectors are then clustered via k-means++ to automatically generate high-quality information abstractions. Notably, WEVA operates without any domain priors or pretraining, and consistently outperforms baseline approaches based on equity and rank across three structurally diverse games, reducing exploitability by over 80%. The method demonstrates strong generalization, high efficiency, and broad applicability.

Abstract

Information abstraction reduces the computational cost of solving imperfect-information games by clustering information sets into a smaller number of \emph{buckets}. Existing methods either rely on domain-specific features such as rank or equity, which are inapplicable to games with non-standard payoff structures, or require expensive offline neural-network training on billions of samples. We propose \textbf{Warm-up Expected Value-based Abstraction (WEVA)}, a simple yet effective alternative: run a small number of Counterfactual Regret Minimization (CFR) iterations on the full game as a \emph{warm-up} phase, extract per-hand expected value features at every decision node, form a depth-weighted multi-node feature vector, and apply $k$-means++ clustering to obtain the abstraction mapping. WEVA requires no domain knowledge, no pre-training, and incurs only a small overhead on top of the abstract-game solve. Experiments on three structurally diverse games, with different bucket numbers and CFR variants, show that WEVA consistently outperforms equity-based and rank-based abstractions, reducing exploitability by up to over $80\%$. Surprisingly, as few as $W{=}10$ warm-up iterations already produce abstractions that outperform existing information abstraction methods in most settings. These results establish WEVA as an \emph{effective, efficient, and general} approach to information abstraction in imperfect-information extensive-form games.

Problem

• imperfect-information games
• information abstraction
• extensive-form games
• computational efficiency
• domain-independence

Innovation

• information abstraction
• imperfect-information games
• Counterfactual Regret Minimization
• WEVA
• k-means++ clustering