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.
@article{arxiv.2205.03504,
title = {Reinforcement Learning Approach to Estimation in Linear Systems},
author = {Minyue Fu},
journal= {arXiv preprint arXiv:2205.03504},
year = {2022}
}