Multi Session Budget Optimization

Multi-session budget optimization addresses the challenge of efficiently allocating resources across multiple sequential decision-making processes, aiming to maximize overall utility while adhering to a fixed total budget. Current research focuses on developing algorithms, often employing reinforcement learning or variations of upper confidence bound methods, to dynamically adjust resource allocation across sessions and within each session, considering factors like varying resource availability and uncertainties in outcomes. This field is significant for its applications in diverse areas such as online advertising, federated learning, and experimental design, improving resource efficiency and decision-making in these contexts.