  We study the problem of multi-agent control of a dynamical system with known
dynamics and adversarial disturbances. Our study focuses on optimal control
without centralized precomputed policies, but rather with adaptive control
policies for the different agents that are only equipped with a stabilizing
controller. We give a reduction from any (standard) regret minimizing control
method to a distributed algorithm. The reduction guarantees that the resulting
distributed algorithm has low regret relative to the optimal precomputed joint
policy. Our methodology involves generalizing online convex optimization to a
multi-agent setting and applying recent tools from nonstochastic control
derived for a single agent. We empirically evaluate our method on a model of an
overactuated aircraft. We show that the distributed method is robust to failure
and to adversarial perturbations in the dynamics.
