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.
翻译:最近对线性系统的学习控制受到了很多关注。 在大众环境中,真正的动态模型对决策者并不熟悉,需要通过对系统应用控制投入进行互动学习。与关于对单一系统进行适应性控制的高效强化学习政策的成熟文献不同,目前没有关于对多个系统进行联合学习的成熟文献,特别是,快速和可靠的联合稳定化这一重要问题仍然没有得到解决,这项工作的重点也是如此。我们建议采用一种新型的基于学习的联合稳定算法,以便从不稳定的状态轨迹数据中迅速学习所有正在研究的系统的稳定政策。所述程序非常有效,在极短的时间内稳定动态系统的组合。