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

Information abstraction reduces the computational cost of solving imperfect-information games by clustering information sets into a smaller number of 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 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 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 effective, efficient, and general approach to information abstraction in imperfect-information extensive-form games.