论文标题
多个未知线性系统的基于联合学习的稳定
Joint Learning-Based Stabilization of Multiple Unknown Linear Systems
论文作者
论文摘要
基于学习的线性系统的控制最近受到了很多关注。在流行的设置中，真正的动态模型是决策者未知的，需要通过将控制输入应用于系统来交互学习。与对单个系统的自适应控制有效的加强学习政策的成熟文献不同，目前尚无多个系统的联合学习结果。特别是，快速和可靠的联合稳定化的重要问题仍然没有解决，这项工作的重点也是如此。我们提出了一种新型的基于联合学习的稳定算法，用于从不稳定状态轨迹的数据中快速学习所有研究系统的稳定策略。所提出的程序被证明是有效的，因此它可以在非常短的时间内稳定动力系统的家族。
Learning-based control of linear systems received a lot of attentions recently. In popular settings, the true dynamical models are unknown to the decision-maker and need to be interactively learned by applying control inputs to the systems. Unlike the matured literature of efficient reinforcement learning policies for adaptive control of a single system, results on joint learning of multiple systems are not currently available. Especially, the important problem of fast and reliable joint-stabilization remains unaddressed and so is the focus of this work. We propose a novel joint learning-based stabilization algorithm for quickly learning stabilizing policies for all systems understudy, from the data of unstable state trajectories. The presented procedure is shown to be notably effective such that it stabilizes the family of dynamical systems in an extremely short time period.
