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The Complexity of POMDPs with Long-run Average Objectives

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ziliotto.pdf (187.3Kb)
Date
2020
Publisher city
Paris
Link to item file
https://hal.archives-ouvertes.fr/hal-02268862
Dewey
Analyse
Sujet
POMDPs; dynamic systems
Journal issue
Mathematics of Operations Research
Publication date
2020
Article pages
15
Publisher
INFORMS
URI
https://basepub.dauphine.fr/handle/123456789/20659
Collections
  • CEREMADE : Publications
Metadata
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Author
Chatterjee, Krishnendu
259628 Institute of Science and Technology [Austria] [IST Austria]
Saona, Raimundo
Ziliotto, Bruno
60 CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Type
Article accepté pour publication ou publié
Abstract (EN)
We study the problem of approximation of optimal values in partially-observable Markov decision processes (POMDPs) with long-run average objectives. POMDPs are a standard model for dynamic systems with probabilistic and nondeterministic behavior in uncertain environments. In long-run average objectives rewards are associated with every transition of the POMDP and the payoff is the long-run average of the rewards along the executions of the POMDP. We establish strategy complexity and computational complexity results. Our main result shows that finite-memory strategies suffice for approximation of optimal values, and the related decision problem is recursively enumerable complete.

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