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We propose a policy iteration algorithm for solving the multiplicative noiselinear quadratic output feedback design problem. The algorithm solves a set ofcoupled Riccati equations for estimation and control arising from a partiallyobservable Markov decision process (POMDP) under a class of linear dynamiccontrol policies. We show in numerical experiments far faster convergence thana value iteration algorithm, formerly the only known algorithm for solving thisclass of problem. The results suggest promising future research directions forpolicy optimization algorithms in more general POMDPs, including the potentialto develop novel approximate data-driven approaches when model parameters arenot available.

Policy Iteration for Multiplicative Noise Output Feedback Control