Recent Advances in Average-Reward Restless Bandits

Abstract: We consider the infinite-horizon, average-reward restless bandit problem, where a central challenge is designing computationally efficient policies that achieve a diminishing optimality gap as the number of arms, N, grows large. Existing policies, including the renowned Whittle index policy, all rely on a global attractor property (GAP) assumption to achieve asymptotic optimality, which is a complex and difficult-to-verify assumption. In this talk, I will present new policy designs that depart significantly from existing policies, and our policies completely remove this long-standing GAP assumption. Our work offers new policy design methodologies for high-dimensional stochastic systems and new insights into guaranteeing global convergence (avoiding undesirable attractors or cycles).

Bio: Weina Wang is an Associate Professor in the Computer Science Department at Carnegie Mellon University. Her research lies in the broad area of applied probability, with a focus on decision-making in large stochastic systems. She was a joint postdoctoral research associate in the Coordinated Science Lab at the University of Illinois at Urbana-Champaign, and in the School of ECEE at Arizona State University, from 2016 to 2018. She received her Ph.D. degree in Electrical Engineering from Arizona State University in 2016, and her Bachelor’s degree from the Department of Electronic Engineering at Tsinghua University in 2009. Her dissertation received the Dean’s Dissertation Award in the Ira A. Fulton Schools of Engineering at Arizona State University in 2016. She received the Kenneth C. Sevcik Outstanding Student Paper Award at ACM SIGMETRICS 2016, the Best Paper Award at ACM MobiHoc 2022, an NSF CAREER award in 2022, and the ACM SIGMETRICS Rising Star Research Award in 2023.

*** The event will take place in a hybrid format. The location for in-person attendance will be room 3427 EECS. Attendance will also be available via Zoom.

Join Zoom Meeting: https://umich.zoom.us/j/93679028340

Meeting ID: 936 7902 8340

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Zoom Passcode information is available upon request to Kristi Rieger ([email protected]).