时间：9月23日（周二）下午16:00-17:30

地点：教一1301 国家治理大数据和人工智能创新平台 大研讨室

主讲人简介

王文佳

香港科技大学（广州）信息枢纽数据科学与分析学域的助理教授

2018年8月获得佐治亚理工学院工业工程系博士学位。研究方向包括不确定性量化、随机仿真、机器学习、非参数统计和计算机实验。目前已在统计学、机器学习、管理学顶级期刊、会议JASA，JMLR，Management Science，Technometrics，NeurIPS，ICLR，ICML等发表多篇文章。

内容概要

Due to the inability to interact with the environment, offline reinforcement learning (RL) methods face the challenge of estimating the Out-of-Distribution (OOD) points. Existing methods for addressing this issue either control policy to exclude the OOD action or make the Q-function pessimistic. However, these methods can be overly conservative or fail to identify OOD areas accurately. In this talk, I will be discussing our recent advancements in offline reinforcement learning, specifically focusing on the utilization of generative models such as GAN and diffusion models. Our proposed methods are evaluated on the D4RL benchmarks and have demonstrated significant improvements across numerous tasks. Theoretical results are provided for performance guarantee.