On the complexity of finite memory policies for markov decision processes

Danièle Beauquier, Dima Burago, Anatol Slissenko

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

We consider some complexity questions concerning a model of uncertainty known as Markov decision processes. Our results concern the problem of constructing optimal policies under a criterion Of optimality defined in terms of constraints on the behavior of the process. The constraints are described by regular languages, and the motivation goes from robot motion planning. It is known that, in the case of perfect information, optimal policies under the traditional cost criteria can be found among Markov policies and in polytime. We show, firstly, that for the behavior criterion optimal policies are not Markovian for finite as well as infinite horizon. On the other hand, optimal policies in this case lie in the class of finite memory policies defined in the paper, and can be found in polytime. We remark that in the case of partial information, finite memory policies cannot be optimal in the general situation. Nevertheless, the class of finite memory policies seems to be of interest for probabilistic policies: though probabilistic policies are not better than deterministic ones in the general class of history remembering policies, the former ones can be better in the class of finite memory policies.

Original languageEnglish (US)
Title of host publicationMathematical Foundations of Computer Science 1995 - 20th International Symposium, MFCS 1995, Proceedings
EditorsJiri Wiedermann, Petr Hajek
PublisherSpringer Verlag
Pages191-200
Number of pages10
ISBN (Print)3540602461, 9783540602460
DOIs
StatePublished - 1995
Event20th International Symposium on Mathematical Foundations of Computer Science, MFCS 1995 - Prague, Czech Republic
Duration: Aug 28 1995Sep 1 1995

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume969
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other20th International Symposium on Mathematical Foundations of Computer Science, MFCS 1995
CountryCzech Republic
CityPrague
Period8/28/959/1/95

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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    Beauquier, D., Burago, D., & Slissenko, A. (1995). On the complexity of finite memory policies for markov decision processes. In J. Wiedermann, & P. Hajek (Eds.), Mathematical Foundations of Computer Science 1995 - 20th International Symposium, MFCS 1995, Proceedings (pp. 191-200). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 969). Springer Verlag. https://doi.org/10.1007/3-540-60246-1_125