Effective, Efficient, and General Information Abstraction for Imperfect-Information Extensive-Form Games
2026-05-11 • Computer Science and Game Theory
Computer Science and Game Theory
AI summaryⓘ
The authors introduce WEVA, a new way to simplify complex games where players have hidden information. Instead of relying on game-specific knowledge or heavy training, WEVA uses a short initial run of a learning algorithm to gather helpful features, then groups similar situations together using clustering. Their tests on different games show that WEVA makes solving these games more accurate and efficient than older methods, even with very few initial learning steps. This makes WEVA a practical and broadly useful tool for managing hidden information in large games.
imperfect-information gamesinformation abstractionCounterfactual Regret Minimization (CFR)expected valuek-means++ clusteringexploitabilityextensive-form gamesfeature extractiongame theoryreinforcement learning
Authors
Boning Li, Longbo Huang
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.