普林斯顿大学王梦迪教授讲座预告

来源：江苏省运筹学会

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江苏省运筹学会联合南京大学工程管理学院、江苏省管理科学与工程学科联盟邀请美国普林斯顿大学王梦迪教授于6月23日上午9：00-10：30开展一场与运筹优化、强化学习相关的在线讲座。欢迎参加！

参会方式

ZOOM会议ID：925 0812 1339

会议密码：559463

 题目：On the statistical complexity of reinforcement learning

报告人

王梦迪教授， 普林斯顿大学电子工程系、统计与机器学习中心

时间

6月23日上午9:00-10：30

摘要

Recent years have witnessed increasing empirical successes in reinforcement learning (RL). However, many theoretical questions about RL were not well understood. For example, how many observations are needed and sufficient for learning a good policy? What is the regret of online learning with function approximation in a Markov decision process (MDP)? From logged history generated by unknown behavior policies, how do we optimally estimate the value of a new policy? In this talk, I will review some recent results addressing these questions, such as the minimax-optimal sample complexities for solving MDP from a generative model, minimax-optimal off-policy evaluation by regression, and regret of online RL with nonparametric model estimation.

报告

专家

简介

Mengdi Wang is an associate professor at the Department of Electrical Engineering and Center for Statistics and Machine Learning at Princeton University. Her research focuses on stochastic optimization and theoretical 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 visiting Google DeepMind.

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编辑：南京师范大学李婷