Regret Minimization with Adaptive Opponents in Repeated Games

Regret minimization strategies in repeated games are being reevaluated to account for adaptive opponents who adjust their tactics based on past interactions. The traditional external regret metric is insufficient for capturing such dynamic behavior, prompting the introduction of Repeated Policy Regret (RP-Regret) as a more suitable game-theoretic metric¹. This new approach acknowledges the importance of counterfactual reasoning in assessing player performance. By incorporating RP-Regret, researchers can better understand the complexities of adaptive decision-making in repeated games. The implications of this work extend beyond the realm of game theory, as it can inform the development of more effective strategies for interacting with adaptive entities, such as state-aligned threat actors. So what matters to practitioners is that this research can help them refine their approaches to competitive and adversarial situations, where anticipating and responding to an opponent's adaptability is crucial.