Deep Reinforcement Learning for Optimization at Early Design Stages-Reference-Cited by-同舟云学术

Deep Reinforcement Learning for Optimization at Early Design Stages

Published:2023-02 Issue:1 Volume:40 Page:43-51
ISSN:2168-2356
Container-title:IEEE Design & Test
language:
Short-container-title:IEEE Des. Test

Author:

Servadei Lorenzo¹^ORCID,Lee Jin Hwa²^ORCID,Arjona Medina Jose A.³^ORCID,Werner Michael⁴^ORCID,Hochreiter Sepp³^ORCID,Ecker Wolfgang⁴,Wille Robert³^ORCID

Affiliation:

1. Infineon Technologies AG, Neubiberg, Germany

2. Technical University of Munich, Munich, Germany

3. Johannes Kepler University Linz, Linz, Austria

4. Infineon Technologies AG, Munich, Germany

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Hardware and Architecture,Software

Link

http://xplorestaging.ieee.org/ielx7/6221038/10024350/09687589.pdf?arnumber=9687589

Reference12 articles.

1. Heuristics from Nature for Hard Combinatorial Optimization Problems

2. Accurate Cost Estimation of Memory Systems Utilizing Machine Learning and Solutions from Computer Vision for Design Automation

3. Ranked reward: Enabling self-play reinforcement learning for combinatorial optimization;laterre;arXiv 1807 01672,2018

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