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Risk-Sensitive Average Optimality in Markov Decision Chains

In: Operations Research Proceedings 2007

Author

Listed:
  • Karel Sladký

    (Institute of Information Theory and Automation)

  • Raúl Montes-de-Oca

    (Universidad Autónoma Metropolitana)

Abstract

We consider a Markov decision chain X = {X n, n = 0, 1, ...} with finite state space $$ \mathcal{I} $$ = {1, 2, ...,N} and a finite set $$ \mathcal{A}_i $$ = {1, 2, ...,K i} of possible decisions (actions) in state i ∈ $$ \mathcal{I} $$ . Supposing that in state i ∈ $$ \mathcal{I} $$ action k ∈ $$ \mathcal{A}_i $$ is selected, then state j is reached in the next transition with a given probability p ij k and one-stage transition reward r ij will be accrued to such transition.

Suggested Citation

  • Karel Sladký & Raúl Montes-de-Oca, 2008. "Risk-Sensitive Average Optimality in Markov Decision Chains," Operations Research Proceedings, in: Jörg Kalcsics & Stefan Nickel (ed.), Operations Research Proceedings 2007, pages 69-74, Springer.
    • Handle: RePEc:spr:oprchp:978-3-540-77903-2_11
    DOI: 10.1007/978-3-540-77903-2_11
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    Cited by:

    1. Daniel Hernández Hernández & Diego Hernández Bustos, 2017. "Local Poisson Equations Associated with Discrete-Time Markov Control Processes," Journal of Optimization Theory and Applications, Springer, vol. 173(1), pages 1-29, April.

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