Reinforcement Learning Approach to Estimation in Linear Systems

Minyue Fu

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arXiv · 2205.03504

Reinforcement Learning Approach to Estimation in Linear Systems

Minyue Fu

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Abstract

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.

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Minyue Fu. 2022-05-06. Reinforcement Learning Approach to Estimation in Linear Systems. https://arxiv.org/abs/2205.03504

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