Electrical Engineering and Systems Science > Systems and Control
arXiv:2406.11057 (eess)
[Submitted on 16 Jun 2024 (v1), last revised 2 Dec 2024 (this version, v2)]
Title:Design of Interacting Particle Systems for Fast Linear Quadratic RL
View a PDF of the paper titled Design of Interacting Particle Systems for Fast Linear Quadratic RL, by Anant A Joshi and 4 other authors
View PDFAbstract:This paper is concerned with the design of algorithms based on systems of interacting particles to represent, approximate, and learn the optimal control law for reinforcement learning (RL). The primary contribution is that convergence rates are greatly accelerated by the interactions between particles. Theory focuses on the linear quadratic stochastic optimal control problem for which a complete and novel theory is presented. Apart from the new algorithm, sample complexity bounds are obtained, and it is shown that the mean square error scales as $1/N$ where $N$ is the number of particles. The theoretical results and algorithms are illustrated with numerical experiments and comparisons with other recent approaches, where the faster convergence of the proposed algorithm is numerically demonstrated.
| Subjects: | Systems and Control (eess.SY) |
| Cite as: | arXiv:2406.11057 [eess.SY] |
| (orarXiv:2406.11057v2 [eess.SY] for this version) | |
| https://doi.org/10.48550/arXiv.2406.11057 arXiv-issued DOI via DataCite |
Submission history
From: Anant A. Joshi [view email][v1] Sun, 16 Jun 2024 19:58:23 UTC (2,075 KB)
[v2] Mon, 2 Dec 2024 04:53:50 UTC (2,138 KB)
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View a PDF of the paper titled Design of Interacting Particle Systems for Fast Linear Quadratic RL, by Anant A Joshi and 4 other authors
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