Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Off-Policy Q-Learning for Data-Based Discrete-Time LQR
  Problems   This paper introduces and analyzes an improved Q-learning algorithm for
discrete-time linear time-invariant systems. The proposed method does not
require any knowledge of the system dynamics, and it enjoys significant
efficiency advantages over other data-based optimal control methods in the
literature. This algorithm can be fully executed off-line, as it does not
require to apply the current estimate of the optimal input to the system as in
on-policy algorithms. It is shown that a persistently exciting input, defined
from an easily tested matrix rank condition, guarantees the convergence of the
algorithm. Moreover, the method avoids the use of linear matrix inequalities
(LMIs) for control design, decreasing the corresponding computational
complexity. A data-based method is proposed to design the initial stabilizing
feedback gain that the algorithm requires. Robustness of the algorithm in the
presence of noisy measurements is analyzed. Both theoretical and simulation
comparisons are performed to show the advantages of this algorithm against
other model-free control design methods.

28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas