Learning Decentralized Linear Quadratic Regulator with $\sqrt{T}$ Regret

arXiv:2210.08886v3 Announce Type: replace-cross
Abstract: We propose an online learning algorithm that adaptively designs a decentralized linear quadratic regulator when the system model is unknown a priori and new data samples from a single system trajectory become progressively available. The algorithm uses a disturbance-feedback representation of state-feedback controllers coupled with online convex optimization with memory and delayed feedback. Under the assumption that the system is stable or given a known stabilizing controller, we show that our controller enjoys an expected …

abstract algorithm arxiv become cs.lg cs.sy data decentralized designs eess.sy feedback linear math.oc online learning regulator representation samples state the algorithm trajectory type