Learning for Decision and Control in Stochastic Networks

“This monograph gives an overview of a class of algorithms for optimization of queuing networks in wireless and related networks. … This is followed by approaches based on multi-armed bandits and the approaches that use standard reinforcement learning algorithms grounded in the underlying Markov decision theoretic framework. It concludes with some pointers for future work.” (Vivek S. Borkar, Mathematical Reviews, August, 2024)

Longbo Huang, Ph.D. is an Associate Professor at the Institute for Interdisciplinary Information Sciences (IIIS) at Tsinghua University, Beijing, China. He received his Ph.D. in EE from the University of Southern California, and then worked as a postdoctoral researcher in the EECS dept. at University of California at Berkeley before joining IIIS. Dr. Huang previously held visiting positions at the LIDS lab at MIT, the Chinese University of Hong Kong, Bell-labs France, and Microsoft Research Asia (MSRA). He was also a visiting scientist at the Simons Institute for the Theory of Computing at UC Berkeley in Fall 2016. Dr. Huang’s research focuses on decision intelligence (AI for decisions), including deep reinforcement learning, online learning and reinforcement learning, learning-augmented network optimization, distributed optimization and machine learning.