Distributed Reinforcement Learning for Decentralized Linear Quadratic Control: A Derivative-Free Policy Optimization Approach

Yingying Li; Yujie Tang; Runyu Zhang; Na Li

Distributed Reinforcement Learning for Decentralized Linear Quadratic Control: A Derivative-Free Policy Optimization Approach

Yingying Li, Yujie Tang, Runyu Zhang, Na Li

Research output: Contribution to journal › Conference article › peer-review

Original language	English (US)
Pages (from-to)	814
Number of pages	1
Journal	Proceedings of Machine Learning Research
Volume	120
State	Published - 2020
Externally published	Yes
Event	2nd Annual Conference on Learning for Dynamics and Control, L4DC 2020 - Berkeley, United States Duration: Jun 10 2020 → Jun 11 2020

ASJC Scopus subject areas

Artificial Intelligence
Software
Control and Systems Engineering
Statistics and Probability

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@article{778a2bf6e2934f6eb1658f28c6c15b0f,

title = "Distributed Reinforcement Learning for Decentralized Linear Quadratic Control: A Derivative-Free Policy Optimization Approach",

author = "Yingying Li and Yujie Tang and Runyu Zhang and Na Li",

note = "Funding Information: ∗ This work was supported by NSF CAREER 1553407, AFOSR YIP, ONR YIP, and OSR-2019-CoE-NEOM-4178.10. The full version can be found at https://arxiv.org/abs/1912.09135. † The first two authors contribute equally.; 2nd Annual Conference on Learning for Dynamics and Control, L4DC 2020 ; Conference date: 10-06-2020 Through 11-06-2020",

year = "2020",

language = "English (US)",

volume = "120",

pages = "814",

journal = "Proceedings of Machine Learning Research",

issn = "2640-3498",

publisher = "ML Research Press",

}

TY - JOUR

T1 - Distributed Reinforcement Learning for Decentralized Linear Quadratic Control

T2 - 2nd Annual Conference on Learning for Dynamics and Control, L4DC 2020

AU - Li, Yingying

AU - Tang, Yujie

AU - Zhang, Runyu

AU - Li, Na

N1 - Funding Information: ∗ This work was supported by NSF CAREER 1553407, AFOSR YIP, ONR YIP, and OSR-2019-CoE-NEOM-4178.10. The full version can be found at https://arxiv.org/abs/1912.09135. † The first two authors contribute equally.

PY - 2020

Y1 - 2020

UR - http://www.scopus.com/inward/record.url?scp=85161155324&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85161155324&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85161155324

SN - 2640-3498

VL - 120

SP - 814

JO - Proceedings of Machine Learning Research

JF - Proceedings of Machine Learning Research

Y2 - 10 June 2020 through 11 June 2020

ER -

Distributed Reinforcement Learning for Decentralized Linear Quadratic Control: A Derivative-Free Policy Optimization Approach

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