Sub Optimality Gap

The sub-optimality gap, representing the difference in performance between an optimal solution and a given solution, is a central concept in various machine learning fields, particularly reinforcement learning and bandit problems. Current research focuses on deriving tighter bounds on this gap, often dependent on problem-specific parameters like the minimum gap between actions or the misspecification level of the model, and developing algorithms that leverage these bounds to improve efficiency and performance. This work is significant because understanding and minimizing the sub-optimality gap directly translates to improved algorithm design and more efficient resource utilization in diverse applications, ranging from online advertising to robotics.