Decoupling Optimization for Complex PDN Structures Using Deep Reinforcement Learning-Reference-Cited by-同舟云学术

Decoupling Optimization for Complex PDN Structures Using Deep Reinforcement Learning

Author:

Zhang Ling¹^ORCID,Jiang Li¹^ORCID,Juang Jack²,Yang Zhiping³^ORCID,Li Er-Ping¹^ORCID,Hwang Chulsoon²^ORCID

Affiliation:

1. College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China

2. Electromagnetic Compatibility Laboratory, Missouri University of Science and Technology, Rolla, MO, USA

3. Google Inc., Mountain View, CA, USA

Funder

Fellowship of China Postdoctoral Science Foundation

Postdoctoral Science Preferential Funding of Zhejiang Province, China

Zhejiang Provincial Natural Science Foundation of China

Natural Science Foundation of China

National Science Foundation

Google Faculty Research Award

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Condensed Matter Physics,Radiation

Link

Reference33 articles.

5. On the properties of the softmax function with application in game theory and reinforcement learning;gao;arXiv 1704 00805,2017

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