Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data-Based Optimal Control of Multi-Agent Systems: A Reinforcement
  Learning Design Approach   This paper studies optimal consensus tracking problem of heterogeneous linear
multi-agent systems. By introducing tracking error dynamics, the optimal
tracking problem is reformulated as finding a Nash-equilibrium solution of a
multi-player games, which can be done by solving associated coupled
Hamilton-Jacobi (HJ) equations. A data-based error estimator is designed to
obtain the data-based control for the multi-agent systems. Using the quadratic
functional to approximate the every agent's value function, we can obtain the
optimal cooperative control by input-output (I/O) $Q$-learning algorithm with
value iteration technique in the least-square sense. The control law solves the
optimal consensus problem for multi-agent systems with measured input-output
information, and does not rely on the model of multi-agent systems. A numerical
example is provided to illustrate the effectiveness of the proposed algorithm.

13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph