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Discounted Cost Markov Decision Processes with a Constraint

Published online by Cambridge University Press:  27 July 2009

Kazuyoshi Wakuta
Affiliation:
Nagaoka Technical College, 888 Nishikatakai, Nagaoka, Niigata 940, Japan

Abstract

We consider a discounted cost Markov decision process with a constraint. Relating this to a vector-valued Markov decision process, we prove that there exists a constrained optimal randomized semistationary policy if there exists at least one policy satisfying a constraint. Moreover, we present an algorithm by which we can find the constrained optimal randomized semistationary policy, or we can discover that there exist no policies satisfying a given constraint.

Type
Research Article
Copyright
Copyright © Cambridge University Press 1998

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References

1.Altman, E. (1994). Denumerable constrained Markov decision processes and finite approximations. Mathematics of Operations Research 19: 169191.CrossRefGoogle Scholar
2.Altman, E. & Shwartz, A. (1991). Sensitivity of constrained Markov decision processes. Annals of Operations Research 32: 122.CrossRefGoogle Scholar
3.Beutler, F.J. & Ross, K.W. (1985). Optimal policies for controlled Markov chains with a constraint. Journal of Mathematical Analysis and Applications 112: 236252.CrossRefGoogle Scholar
4.Beutler, F.J. & Ross, K.W. (1986). Time-average optimal constrained semi-Markov decision processes. Advances in Applied Probability 18: 341359.CrossRefGoogle Scholar
5.Chitgopekar, S.S. (1975). Denumerable state Markovian sequential control processes: On randomizations of optimal policies. Naval Research Logistics Quarterly 22: 567573.CrossRefGoogle Scholar
6.Frid, E.B. (1972). On optimal strategies in control problems with constraints. Theory of Probability and Its Applications 17: 188192.CrossRefGoogle Scholar
7.Hinderer, K. (1970). Foundations of non-stationary dynamic programming with discrete time parameter. Berlin: Springer-Verlag.CrossRefGoogle Scholar
8.Kallenberg, L.C.M. (1983). Linear programming and finite Markovian control problems. In Mathematical Centre Tracts 148. Amsterdam: CWI.Google Scholar
9.Liu, J. & Liu, K. (1994). Markov decision programming with constraints. Acta Mathematicae Applicatae Sinica 10: 111.CrossRefGoogle Scholar
10.Puterman, M.L. (1994). Markov decision processes. New York: Wiley.CrossRefGoogle Scholar
11.Sennott, L.I. (1991). Constrained discounted Markov decision chains. Probability in the Engineering and Informational Sciences 5: 463475.Google Scholar
12.Stoer, J. & Witzgall, C. (1970). Convexity and optimization infinite dimensions I. Berlin: Springer-Verlag.CrossRefGoogle Scholar
13.Wakuta, K. (1995). Vector-valued Markov decision processes and the systems of linear inequalities. Stochastic Processes and Their Applications 56: 159169.CrossRefGoogle Scholar
14.Wakuta, K. (1996). A new class of policies in vector-valued Markov decision processes. Journal of Mathematical Analysis and Applications 202: 623628.CrossRefGoogle Scholar