Approximate Newton Policy Gradient Algorithms-Reference-Cited by-同舟云学术

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Approximate Newton Policy Gradient Algorithms

Published:2023-09-26 Issue:5 Volume:45 Page:A2585-A2609
ISSN:1064-8275
Container-title:SIAM Journal on Scientific Computing
language:en
Short-container-title:SIAM J. Sci. Comput.

Author:

Li Haoya¹^ORCID,Gupta Samarth²,Yu Hsiangfu³,Ying Lexing⁴^ORCID,Dhillon Inderjit⁵

Affiliation:

1. Department of Mathematics, Stanford University, Stanford, CA 94305 USA.

2. Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213 USA.

3. Amazon Search, Palo Alto, CA 94303 USA.

4. Department of Mathematics, Stanford University, Stanford, CA 94305-2125 USA, and Amazon Search, Palo Alto, CA 94303 USA.

5. Department of Computer Sciences, University of Texas at Austin, Austin, TX 78712-1188 USA, and Google.

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Applied Mathematics,Computational Mathematics

Reference20 articles.

1. Convex Optimization

2. A characterization of superlinear convergence and its application to quasi-Newton methods

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Accelerating Primal-Dual Methods for Regularized Markov Decision Processes;SIAM Journal on Optimization;2024-02-20

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