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We give algorithms for designing near-optimal sparse controllers using policygradient with applications to control of systems corrupted by multiplicativenoise, which is increasingly important in emerging complex dynamical networks.Various regularization schemes are examined and incorporated into theoptimization by the use of gradient, subgradient, and proximal gradientmethods. Numerical experiments on a large networked system show that thealgorithms converge to performant sparse mean-square stabilizing controllers.

Sparse optimal control of networks with multiplicative noise via policy gradient