Sparse optimal control of networks with multiplicative noise via policy gradient
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Publication:6319725
arXiv1905.13548MaRDI QIDQ6319725
Author name not available (Why is that?)
Publication date: 28 May 2019
Abstract: We give algorithms for designing near-optimal sparse controllers using policy gradient with applications to control of systems corrupted by multiplicative noise, which is increasingly important in emerging complex dynamical networks. Various regularization schemes are examined and incorporated into the optimization by the use of gradient, subgradient, and proximal gradient methods. Numerical experiments on a large networked system show that the algorithms converge to performant sparse mean-square stabilizing controllers.
Has companion code repository: https://github.com/TSummersLab/polgrad-multinoise
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