An Online Actor–Critic Algorithm with Function Approximation for Constrained Markov Decision Processes

We develop an online actor–critic reinforcement learning algorithm with function approximation for a problem of control under inequality constraints. We consider the long-run average cost Markov decision process (MDP) framework in which both the objective and the constraint functions are suitable po...

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Veröffentlicht in:	Journal of optimization theory and applications Jg. 153; H. 3; S. 688 - 708
Hauptverfasser:	Bhatnagar, Shalabh, Lakshmanan, K.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Boston Springer US 01.06.2012 Springer Nature B.V
Schlagworte:	Algorithms Applications of Mathematics Approximation Asymptotic properties Calculus of Variations and Optimal Control; Optimization Costs Engineering Lagrange multiplier Markov analysis Markov processes Mathematical analysis Mathematical models Mathematics Mathematics and Statistics On-line systems Online Operations Research/Decision Theory Optimization Queues Random variables Theory of Computation Constrained Markov decision processes Function approximation Actor–critic algorithm Long-run average cost criterion
ISSN:	0022-3239, 1573-2878
Online-Zugang:	Volltext
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