Sparse optimal control of networks with multiplicative noise via policy gradient

Benjamin Gravell; Yi Guo; Tyler Summers

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arXiv · 1905.13548

Sparse optimal control of networks with multiplicative noise via policy gradient

Benjamin Gravell·Yi Guo·Tyler Summers

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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.

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Benjamin Gravell, Yi Guo, Tyler Summers. 2019-05-28. Sparse optimal control of networks with multiplicative noise via policy gradient. https://arxiv.org/abs/1905.13548

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