论文标题
线性系统中估算的强化学习方法
Reinforcement Learning Approach to Estimation in Linear Systems
论文作者
论文摘要
本文解决了线性系统的两个重要估计问题，即系统识别和无模型状态估计。我们的重点是具有未知参数的ARMAX模型。我们首先为系统识别提供了一种增强学习算法，并提供保证的一致性。然后，该算法用于为无模型状态估计提供新的解决方案。然后将这些结果应用于在增强学习设置中解决无模型的LQG控制问题。
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.
