  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.
