Speaker: Mengdi Wang Princeton University
Time: 2019-12-27 14:00-2019-12-27 15:00
Venue: FIT Lecture Hall

Abstract:

In this talk we survey recent advances on statistical efficiency and regret of reinforcement learning (RL) when good state representations are available. Motivated by the RL theory, we discuss what should be good state representations for RL and how to  find compact state embeddings from high-dimensional Markov state trajectories. In the spirit of diffusion map for dynamical systems, we propose an efficient method for learning a low-dimensional state embedding and capturing the process's dynamics. State embedding can be used to cluster states into metastable sets, thereby identifying the slow dynamics of a black-box system; as well as to identify interpretable latent states and soft aggregation structures through nonnegative functional factorization and convex hull approximation. We demonstrated these approaches in various data sets including Atari game.

Short Bio:

Mengdi Wang is an associate professor at the Center for Statistics and Machine Learning at Princeton University. She is also affiliated with the Department of Operations Research and Financial Engineering and Department of Computer Science. Her research focuses on data-driven stochastic optimization and applications in machine and reinforcement learning. She received her PhD in Electrical Engineering and Computer Science from Massachusetts Institute of Technology in 2013. At MIT, Mengdi was affiliated with the Laboratory for Information and Decision Systems and was advised by Dimitri P. Bertsekas. Mengdi became an assistant professor at Princeton in 2014. She received the Young Researcher Prize in Continuous Optimization of the Mathematical Optimization Society in 2016 (awarded once every three years), the Princeton SEAS Innovation Award in 2016, the NSF Career Award in 2017, the Google Faculty Award in 2017,  and the MIT Tech Review 35-Under-35 Innovation Award (China region) in 2018. She is currently serving as an associate editor for Operations Research.