用Transformer模型动态识别并打击扑克对手弱点，兼顾安全与收益。
StratFormer: Adaptive Opponent Modeling and Exploitation in Imperfect-Information Games
- 分两阶段训练：先学对手行为模式，再逐步转向针对性反击。
- 对强弱不同对手平均多赚0.106大盲注，最强时达0.821。
- 适合博弈论与强化学习研究者，尤其关注对抗性建模场景。
我们提出StratFormer，一种基于Transformer的元智能体，通过双阶段课程学习，在不完全信息博弈中同时建模与利用对手。第一阶段训练对手建模头，从动作历史中识别行为模式，同时采用博弈论最优（GTO）策略；第二阶段逐步将策略转向最佳应对（BR）策略，由与可利用性相关的对手定制正则化调度引导。架构引入双轮标记——在己方与对手决策点构建特征向量，并结合五种战略情境的桶率特征编码对手倾向。在包含六张牌、两轮下注的Leduc Hold'em上测试，针对六类对手原型（每类分两个强度级别），可利用性范围为每局0.15至1.26大盲注。StratFormer相比GTO平均实现+0.106大盲注/局的利用收益，对高度可利用对手峰值达到+0.821，同时保持接近均衡的安全性。
原文摘要 · Abstract (English)
We present StratFormer, a transformer-based meta-agent that learns to simultaneously model and exploit opponents in imperfect-information games through a two-phase curriculum. The first phase trains an opponent modeling head to identify behavioral patterns from action histories while the agent plays a game-theoretic optimal (GTO) policy. The second phase progressively shifts the policy toward best-response (BR) exploitation, guided by a per-opponent regularization schedule tied to exploitability. Our architecture introduces dual-turn tokens -- feature vectors constructed at both agent and opponent decision points -- coupled with bucket-rate features that encode opponent tendencies across five strategic contexts. On Leduc Hold'em, a small poker variant with six cards and two betting rounds, we test against six opponent archetypes at two strength levels each, with exploitability ranging from 0.15 to 1.26 Big Blinds (BB) per hand. StratFormer achieves an average exploitation gain of +0.106 BB per hand over GTO, with peak gains of +0.821 against highly exploitable opponents, while maintaining near-equilibrium safety.
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