地点：腾讯会议 627-780-673
邀请人：孙海琳教授
Title:
Abstract: We consider stochastic optimal control (SOC) problems where the distribution of randomness is unknown but can be estimated from streaming data. Assuming a parametric form of the randomness distribution, we take a Bayesian approach to estimate the unknown distribution parameter. The Bayesian posterior distribution can be treated as a state augmented to the original state, leading to a higher-dimensional continuous-state SOC problem. While this approach theoretically provides the optimal control policy, it can be challenging to solve numerically. Therefore, we propose an episodic approach that only updates the posterior periodically and solves a Bayesian counterpart problem under the fixed posterior in each period. Theoretical convergence results and computational methods will be discussed.
Bio:
Enlu Zhou is a Fouts Family Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. She received the B.S. degree with highest honors in electrical engineering from Zhejiang University, China, and the Ph.D. degree in electrical engineering from the University of Maryland, College Park. Prior to joining Georgia Tech, she was an assistant professor in the Industrial & Enterprise Systems Engineering Department at the University of Illinois Urbana-Champaign. She is a recipient of the Best Theoretical Paper award at the Winter Simulation Conference, AFOSR Young Investigator award, NSF CAREER award, and INFORMS Outstanding Simulation Publication Award. She has been on the editorial board of Journal of Simulation, IEEE Transactions on Automatic Control, Operations Research, and SIAM Journal on Optimization. She is currently a co-Editor-in-Chief for Journal of Simulation. She is the President of the INFORMS Simulation Society from 2024 to 2026. Her research interests lie in theory, methods, and applications of simulation, stochastic optimization, and stochastic control.
