Regret Minimization with Adaptive Opponents in Repeated Games 事件

Regret Minimization with Adaptive Opponents in Repeated Games arXiv:2606.06486v1 Announce Type: cross Abstract: In this paper, we study regret minimization in repeated games with \emph{adaptive} opponents who can respond based on histories of play. The standard metric of \emph{external regret} in online learning is known to fail to capture such adaptivity. To account for players' counterfactual reasoning, we introduce {\tt Repeated Policy Regret (RP-Regret)}, a game-theoretic metric that measur