Regret Minimizer

Regret minimizers are algorithms designed to make sequential decisions that minimize the difference between cumulative rewards obtained and those achievable with the optimal strategy in hindsight. Current research focuses on extending regret minimization to complex scenarios involving long-term constraints, such as resource limitations or budget management, often employing primal-dual methods or optimistic constraint estimation within bandit frameworks. These advancements are significant for improving online learning in various applications, including resource allocation, online advertising, and game theory, by providing efficient and robust algorithms that handle both stochastic and adversarial environments. The development of parameter-free and adaptive regret minimizers further enhances their practical applicability and theoretical elegance.