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.

翻译：信息抽象通过将信息集聚类为更少的“桶”来降低解决不完美信息博弈的计算成本。现有方法要么依赖等级或权益等特定领域特征（这些特征不适用于具有非标准收益结构的博弈），要么需要对数十亿样本进行昂贵的离线神经网络训练。本文提出**基于期望值的热启动抽象（WEVA）**，一种简单而有效的替代方案：在完整博弈上运行少量反事实遗憾最小化（CFR）迭代作为“热启动”阶段，提取每个决策节点处的单手牌期望值特征，形成深度加权的多节点特征向量，并应用k-means++聚类获得抽象映射。WEVA无需领域知识、无需预训练，且仅在抽象博弈求解基础上增加少量计算开销。在三个结构不同的博弈实验（采用不同桶数量和CFR变体）中，WEVA始终优于基于权益和基于等级的抽象方法，将可剥削性降低超过80%。令人惊讶的是，仅需W=10次热启动迭代产生的抽象，在多数设置中已优于现有信息抽象方法。这些结果确立了WEVA作为不完美信息扩展式博弈中信息抽象的一种“有效、高效且通用”的方法。