Policy gradient (PG) methods and their variants lie at the heart of modern reinforcement learning. Due to the intrinsic non-concavity of value maximization, however, the theoretical underpinnings of PG-type methods have been limited even until recently. In this talk, we discuss both the ineffectiveness and effectiveness of nonconvex policy optimization. On the one hand, we demonstrate that the popular softmax policy gradient method can take exponential time to converge. On the other hand, we show that employing natural policy gradients and enforcing entropy regularization allows for fast global convergence.
10月17日
11:00am - 12:00pm

地点
https://hkust.zoom.us/j/94883840530 (Passcode: hkust)
讲者/表演者
Prof. Yuting WEI
The Wharton School, University of Pennsyvania
The Wharton School, University of Pennsyvania
主办单位
Department of Mathematics
联系方法
付款详情
对象
Alumni, Faculty and staff, PG students, UG students
语言
英语
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Abstract
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