StratFormer: Adaptive Opponent Modeling and Exploitation in Imperfect-Information Games

arXiv:2604.25796v1 Announce Type: new Abstract: 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-opponen