Dynamic Regret Minimization

Dynamic regret minimization addresses the challenge of online decision-making in environments where the optimal strategy changes over time. Current research focuses on developing algorithms that adapt to these changes, often employing techniques like optimistic online mirror descent and variations of follow-the-regularized-leader, with a particular emphasis on analyzing regret bounds under different measures of non-stationarity (e.g., total variation, path length). This field is significant because it provides theoretical foundations and practical algorithms for improving performance in various applications, including reinforcement learning, imitation learning, and control systems operating in non-stationary environments.