  Online optimization has recently opened avenues to study optimal control for
time-varying cost functions that are unknown in advance. Inspired by this line
of research, we study the distributed online linear quadratic regulator (LQR)
problem for linear time-invariant (LTI) systems with unknown dynamics. Consider
a multi-agent network where each agent is modeled as a LTI system. The network
has a global time-varying quadratic cost, which may evolve adversarially and is
only partially observed by each agent sequentially. The goal of the network is
to collectively (i) estimate the unknown dynamics and (ii) compute local
control sequences competitive to the best centralized policy in hindsight,
which minimizes the sum of network costs over time. This problem is formulated
as a regret minimization. We propose a distributed variant of the online LQR
algorithm, where agents compute their system estimates during an exploration
stage. Each agent then applies distributed online gradient descent on a
semi-definite programming (SDP) whose feasible set is based on the agent system
estimate. We prove that with high probability the regret bound of our proposed
algorithm scales as $O(T^{2/3}\log T)$, implying the consensus of all agents
over time. We also provide simulation results verifying our theoretical
guarantee.
