Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges-Reference-Cited by-同舟云学术

Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges

Author:

Chen Xin¹^ORCID,Qu Guannan²^ORCID,Tang Yujie¹^ORCID,Low Steven³^ORCID,Li Na¹^ORCID

Affiliation:

1. School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

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

3. Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA

Funder

NSF CAREER

NSF AI Institute

NSF

Cyber-Physical Systems

Resnick Sustainability Institute for Science, Energy and Sustainability, California Institute of Technology

PIMCO Fellowship

Amazon AI4Science Fellowship

Caltech Center for Autonomous Systems and Technologies

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Computer Science

Link

Reference200 articles.

1. Reward constrained policy optimization;tessler;arXiv 1805 11074,2018

2. Density constrained reinforcement learning;qin;Proc Int Conf Mach Learn,2021

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