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Approximation of Constrained Average Cost Markov Control Processes

Author(s):

T. Sutter, P. Mohajerin Esfahani, J. Lygeros
Conference/Journal:

IEEE Conference on Decision and Control, Los Angeles, USA
Abstract:

This paper considers discrete-time constrained Markov control processes (MCPs) under the long-run expected average cost optimality criterion. For Borel state and action spaces a two-step method is presented to numerically approximate the optimal value of this constrained MCPs. The proposed method employs the infinite-dimensional linear programming (LP) representation of the constrained MCPs. In particular, we establish a bridge from the infinite-dimensional LP characterization to a finite LP in which the performance of the approximation is quantified. Finally, the applicability and performance of the theoretical results are demonstrated on an LQG example.

Year:

2014
Type of Publication:

(01)Article
Supervisor:



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% Autogenerated BibTeX entry
@InProceedings { SutEsf:2014:IFA_4797,
    author={T. Sutter and P. Mohajerin Esfahani and J. Lygeros},
    title={{Approximation of Constrained Average Cost Markov Control
	  Processes}},
    booktitle={IEEE Conference on Decision and Control},
    pages={},
    year={2014},
    address={Los Angeles, USA},
    month=dec,
    url={http://control.ee.ethz.ch/index.cgi?page=publications;action=details;id=4797}
}
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