Prof. Vaneet Aggarwal
Title: Non-linear Reinforcement Learning: A Non-Markovian Approach
Abstract: Reinforcement Learning (RL) is being increasingly applied to optimize complex functions that may have a stochastic component. RL is extended to multi-agent systems to find policies to optimize systems that require agents to coordinate or to compete under the umbrella of Multi-Agent RL (MARL). A crucial factor in the success of RL is that the optimization problem is represented as the expected sum of rewards, which allows the use of backward induction for the solution. However, many real-world problems require a joint objective that is non-linear and dynamic programming cannot be applied directly. For example, in a resource allocation problem, one of the objective is to maximize long-term fairness among the users. This talk addresses the problem of joint objective optimization, where not only the sum of rewards of each agent but a function of the sum of rewards of each agent needs to be optimized. In such cases, the problem is no longer a Markov Decision Process. We propose efficient model-based and model-free approaches for such problem, with provable guarantees. Further, using fairness in cellular base-station scheduling as an example, the proposed algorithms are shown to significantly outperform the state-of-the-art approaches.
Biography: Vaneet Aggarwal received the B.Tech. degree in 2005 from the Indian Institute of Technology, Kanpur, India, and the M.A. and Ph.D. degrees in 2007 and 2010, respectively from Princeton University, Princeton, NJ, USA, all in Electrical Engineering. He is currently an Associate Professor in the School of IE and ECE (by courtesy) at Purdue University, West Lafayette, IN, where he has been since Jan 2015. He was a Senior Member of Technical Staff Research at AT&T Labs-Research, NJ (2010-2014), Adjunct Assistant Professor at Columbia University, NY (2013-2014), and VAJRA Adjunct Professor at IISc Bangalore (2018-2019). His current research interests are in communications and networking, cloud computing, and machine learning. Dr. Aggarwal received Princeton University’s Porter Ogden Jacobus Honorific Fellowship in 2009, the AT&T Vice President Excellence Award in 2012, the AT&T Key Contributor Award in 2013, the AT&T Senior Vice President Excellence Award in 2014, the 2017 Jack Neubauer Memorial Award recognizing the Best Systems Paper published in the IEEE Transactions on Vehicular Technology, and the 2018 Infocom Workshop HotPOST Best Paper Award. He is on the Editorial Board of the IEEE Transactions on Communications, the IEEE Transactions on Green Communications and Networking, and the IEEE/ACM Transactions on Networking.