Regret Algorithm

Regret algorithms aim to minimize the cumulative difference between an agent's performance and that of an optimal strategy, chosen in hindsight, across a sequence of decisions. Current research focuses on extending these algorithms to complex settings, such as contextual bandits, Stackelberg games, and constrained optimization problems, often employing techniques like optimistic optimization, function approximation (e.g., using reproducing kernel Hilbert spaces), and primal-dual methods. These advancements are crucial for improving the efficiency and robustness of online decision-making in various applications, including online advertising, resource allocation, and reinforcement learning. The development of efficient and theoretically sound regret algorithms continues to be a significant area of investigation, driving progress in both theoretical understanding and practical applications.