Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Model-based reinforcement learning for infinite-horizon approximate
  optimal tracking   This paper provides an approximate online adaptive solution to the
infinite-horizon optimal tracking problem for control-affine continuous-time
nonlinear systems with unknown drift dynamics. Model-based reinforcement
learning is used to relax the persistence of excitation condition. Model-based
reinforcement learning is implemented using a concurrent learning-based system
identifier to simulate experience by evaluating the Bellman error over
unexplored areas of the state space. Tracking of the desired trajectory and
convergence of the developed policy to a neighborhood of the optimal policy are
established via Lyapunov-based stability analysis. Simulation results
demonstrate the effectiveness of the developed technique.

9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph