论文标题
控制控制的统计学习理论：有限的样本观点
Statistical Learning Theory for Control: A Finite Sample Perspective
论文作者
论文摘要
这项教程调查概述了统计学习理论中最近的非反应性进步与控制和系统识别相关。尽管在所有控制领域都取得了长足的进步，但对于线性二次调节器的线性系统识别和学习，该理论是最发达的，这是该手稿的重点。从理论的角度来看，这些进步的大部分劳动都在于从现代高维统计和学习理论中调整工具。尽管与有兴趣整合机器学习工具的理论家高度相关，但基础材料并不总是容易访问。为了解决这个问题，我们提供了相关材料的独立介绍，概述了基于最新结果的所有关键思想和技术机械。我们还提出了许多开放问题和未来的方向。
This tutorial survey provides an overview of recent non-asymptotic advances in statistical learning theory as relevant to control and system identification. While there has been substantial progress across all areas of control, the theory is most well-developed when it comes to linear system identification and learning for the linear quadratic regulator, which are the focus of this manuscript. From a theoretical perspective, much of the labor underlying these advances has been in adapting tools from modern high-dimensional statistics and learning theory. While highly relevant to control theorists interested in integrating tools from machine learning, the foundational material has not always been easily accessible. To remedy this, we provide a self-contained presentation of the relevant material, outlining all the key ideas and the technical machinery that underpin recent results. We also present a number of open problems and future directions.
