Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Scalable regret for learning to control network-coupled subsystems with
  unknown dynamics   We consider the problem of controlling an unknown linear quadratic Gaussian
(LQG) system consisting of multiple subsystems connected over a network. Our
goal is to minimize and quantify the regret (i.e. loss in performance) of our
strategy with respect to an oracle who knows the system model. Viewing the
interconnected subsystems globally and directly using existing LQG learning
algorithms for the global system results in a regret that increases
super-linearly with the number of subsystems. Instead, we propose a new
Thompson sampling based learning algorithm which exploits the structure of the
underlying network. We show that the expected regret of the proposed algorithm
is bounded by $\tilde{\mathcal{O}} \big( n \sqrt{T} \big)$ where $n$ is the
number of subsystems, $T$ is the time horizon and the
$\tilde{\mathcal{O}}(\cdot)$ notation hides logarithmic terms in $n$ and $T$.
Thus, the regret scales linearly with the number of subsystems. We present
numerical experiments to illustrate the salient features of the proposed
algorithm.

27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga