Cumulative Regret

Cumulative regret quantifies the difference between an algorithm's performance and the optimal performance achievable in hindsight, a crucial metric in sequential decision-making problems like multi-armed bandits and reinforcement learning. Current research focuses on minimizing cumulative regret in various settings, including those with contextual information, non-stationary rewards, and complex causal relationships, often employing algorithms like UCB, Thompson Sampling, and variations thereof, along with novel estimators and regret bounds. These advancements have significant implications for optimizing online systems, particularly in personalized recommendations, online advertising, and causal inference, where efficient learning from sequential interactions is paramount.