  This paper addresses two important estimation problems for linear systems,
namely system identification and model-free state estimation. Our focus is on
ARMAX models with unknown parameters. We first provide a reinforcement learning
algorithm for system identification with guaranteed consistency. This algorithm
is then used to provide a novel solution to model-free state estimation. These
results are then applied to solving the model-free LQG control problem in the
reinforcement learning setting.
