  We propose a policy iteration algorithm for solving the multiplicative noise
linear quadratic output feedback design problem. The algorithm solves a set of
coupled Riccati equations for estimation and control arising from a partially
observable Markov decision process (POMDP) under a class of linear dynamic
control policies. We show in numerical experiments far faster convergence than
a value iteration algorithm, formerly the only known algorithm for solving this
class of problem. The results suggest promising future research directions for
policy optimization algorithms in more general POMDPs, including the potential
to develop novel approximate data-driven approaches when model parameters are
not available.
