ABSTRACT:

Offline reinforcement learning (RL) is aiming for exploiting the tremendous historical experiences for future decision making. In this talk, we summarize our recent work on how the optimization will be applied to a variety of offline RL problems, including policy evaluation, confidence interval estimation, policy optimization and imitation learning. The derivations not only provide a unified treatment and perspective on many existing methods, more importantly, yield a number of novel RL algorithms towards practical applications.

BIO:

Bo Dai is a senior research scientist in Google Brain. He obtained his Ph.D. from Georgia Tech. He is the recipient of the best paper award of AISTATS 2016. His research interest lies in developing principled (deep) machine learning methods using tools from optimization, especially on reinforcement learning and data-driven algorithm design, as well as various applications.

 Hosted by Professor Quanquan Gu

Via Zoom Webinar