Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Imposing Robust Structured Control Constraint on Reinforcement Learning
  of Linear Quadratic Regulator   This paper discusses learning a structured feedback control to obtain
sufficient robustness to exogenous inputs for linear dynamic systems with
unknown state matrix. The structural constraint on the controller is necessary
for many cyber-physical systems, and our approach presents a design for any
generic structure, paving the way for distributed learning control. The ideas
from reinforcement learning (RL) in conjunction with control-theoretic
sufficient stability and performance guarantees are used to develop the
methodology. First, a model-based framework is formulated using dynamic
programming to embed the structural constraint in the linear quadratic
regulator (LQR) setting along with sufficient robustness conditions.
Thereafter, we translate these conditions to a data-driven learning-based
framework - robust structured reinforcement learning (RSRL) that enjoys the
control-theoretic guarantees on stability and convergence. We validate our
theoretical results with a simulation on a multi-agent network with 6 agents.

26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ