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Article Dans Une Revue Journal of Mathematical Analysis and Applications Année : 2012

Approximation of Markov Decision Processes with General State Space

Résumé

We deal with a discrete-time finite horizon Markov decision process with locally compact Borel state and action spaces, and possibly unbounded cost function. Based on Lipschitz continuity of the elements of the control model, we propose a state and action discretization procedure for approximating the optimal value function and an optimal policy of the original control model. We provide explicit bounds on the approximation errors. Our results are illustrated by a numerical application to a fisheries management problem.

Dates et versions

hal-00648223 , version 1 (05-12-2011)

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Citer

François Dufour, Tomas Prieto-Rumeau. Approximation of Markov Decision Processes with General State Space. Journal of Mathematical Analysis and Applications, 2012, 388 (2), pp.1254-1267. ⟨10.1016/j.jmaa.2011.11.015⟩. ⟨hal-00648223⟩
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