  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.
