Scalable Regret for Learning to Control Network-Coupled Subsystems With Unknown Dynamics-Reference-Cited by-同舟云学术

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Scalable Regret for Learning to Control Network-Coupled Subsystems With Unknown Dynamics

Published:2023-03 Issue:1 Volume:10 Page:2-14
ISSN:2325-5870
Container-title:IEEE Transactions on Control of Network Systems
language:
Short-container-title:IEEE Trans. Control Netw. Syst.

Author:

Sudhakara Sagar¹^ORCID,Mahajan Aditya²^ORCID,Nayyar Ashutosh¹^ORCID,Ouyang Yi³^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA

2. Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada

3. Preferred Networks America, Burlingame, CA, USA

Funder

NSF

Innovation for Defence Excellence and Security

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Control and Optimization,Computer Networks and Communications,Signal Processing,Control and Systems Engineering

Link

https://ieeexplore.ieee.org/ielam/6509490/10075640/9799757-aam.pdf

Reference43 articles.

1. Learning linear-quadratic regulators efficiently with only $\sqrt{T}$ regret;cohen;Proc Int Conf Mach Learn,0

2. Mean field games

3. Optimism-Based Adaptive Regulation of Linear-Quadratic Systems

4. Large-Population Cost-Coupled LQG Problems With Nonuniform Agents: Individual-Mass Behavior and Decentralized $\varepsilon$-Nash Equilibria

5. Regret bounds for robust adaptive control of the linear quadratic regulator;dean;Proc Neural Inf Process Syst,0

Cited by 2 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. Joint learning of linear time-invariant dynamical systems;Automatica;2024-06

2. Joint Learning-Based Stabilization of Multiple Unknown Linear Systems;IFAC-PapersOnLine;2022

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