Title: Joint Learning-Based Stabilization of Multiple Unknown Linear Systems Show Chinese title

Title: 基于联合学习的多个未知线性系统稳定化

Abstract: 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. Show Chinese abstract

Abstract: 基于学习的线性系统控制近年来引起了广泛关注。在常见的设定下，决策者不知道系统的真正动态模型，需要通过施加控制输入与系统交互来学习这些模型。与自适应控制单个系统的高效强化学习策略成熟的文献相比，目前尚无关于多个系统联合学习的结果。特别是，快速且可靠的联合稳定问题仍然未被解决，这也是本工作的重点。我们提出了一种新颖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待研究系统的稳定策略。所提出的程序被证明非常有效，能够在极短的时间内使动力学系统族实现稳定。