The Complexity of POMDPs with Long-run Average Objectives
Chatterjee, Krishnendu; Saona, Raimundo; Ziliotto, Bruno (2020), The Complexity of POMDPs with Long-run Average Objectives, Mathematics of Operations Research, p. 15
TypeArticle accepté pour publication ou publié
External document linkhttps://hal.archives-ouvertes.fr/hal-02268862
Journal nameMathematics of Operations Research
MetadataShow full item record
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
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.
Subjects / KeywordsPOMDPs; dynamic systems
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