Achievable Regret

Achievable regret, in the context of sequential decision-making problems like multi-armed bandits and reinforcement learning, quantifies the performance loss compared to an optimal strategy. Current research focuses on refining regret definitions to better capture learning in complex scenarios, such as those with non-trivial state transitions or Byzantine agents providing faulty information, and developing algorithms like Thompson sampling and variations of UCB that minimize this regret under various constraints. These advancements are significant because they improve the efficiency and robustness of decision-making systems in diverse applications, ranging from online advertising and resource allocation to decentralized control and medical trials. The development of meta-learning techniques further enhances performance by leveraging experience across multiple similar tasks.