Swap Regret

Swap regret, a measure of an agent's performance in repeated games where actions can be changed after observing outcomes, focuses on minimizing the difference in payoff between chosen actions and the best alternative action sequence in hindsight. Current research emphasizes developing efficient algorithms, such as those based on optimistic multiplicative weights update and self-concordant barriers, to achieve low swap regret in various settings, including adversarial environments and high-dimensional prediction problems. These advancements have implications for improving the efficiency of learning dynamics in games, leading to faster convergence to correlated equilibria and better performance in online decision-making scenarios. The development of tighter bounds on swap regret and efficient algorithms for its minimization is crucial for advancing the understanding and application of learning in dynamic strategic interactions.