Regret Rate

Regret rate, in the context of online learning and reinforcement learning, quantifies the cumulative difference between an algorithm's performance and that of an optimal strategy with perfect knowledge. Current research focuses on minimizing regret in various settings, including stochastic and adversarial bandits, linear contextual bandits, and Markov Decision Processes (MDPs), employing algorithms like UCB, linear programming approaches, and kernel methods with neural networks sometimes incorporated. These advancements aim to improve the efficiency and robustness of learning algorithms in dynamic environments, with implications for applications ranging from personalized recommendations to adaptive control systems. The ultimate goal is to develop algorithms with provably optimal or near-optimal regret rates, ensuring efficient learning even under uncertainty and changing conditions.