Online Learning Problem

Online learning addresses the challenge of making sequential decisions in dynamic environments with incomplete information, aiming to minimize cumulative error or maximize reward over time. Current research focuses on developing efficient algorithms for various settings, including those with contextual information, adversarial inputs, and resource constraints, often employing techniques like multi-learner approaches, primal-dual methods, and adaptations of classic algorithms like UCB and FTPL. These advancements are crucial for improving performance in diverse applications such as data pricing, online advertising, and multi-robot task assignment, where real-time adaptation to changing conditions is essential. The field is also exploring the theoretical limits of online learning under different assumptions about data and feedback.