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Finite-Memory Strategies in POMDPs with Long-Run Average Objectives

Author

Listed:
  • Krishnendu Chatterjee

    (Institute of Science and Technology Austria, 3400 Klosterneuburg, Austria)

  • Raimundo Saona

    (Institute of Science and Technology Austria, 3400 Klosterneuburg, Austria)

  • Bruno Ziliotto

    (Centre de Recherche en Mathématiques de la Décision, Centre National de la Recherche Scientifique, Université Paris Dauphine, Université PSL, 75016 Paris, France)

Abstract

Partially observable Markov decision processes (POMDPs) are standard models for dynamic systems with probabilistic and nondeterministic behaviour in uncertain environments. We prove that in POMDPs with long-run average objective, the decision maker has approximately optimal strategies with finite memory. This implies notably that approximating the long-run value is recursively enumerable, as well as a weak continuity property of the value with respect to the transition function.

Suggested Citation

  • Krishnendu Chatterjee & Raimundo Saona & Bruno Ziliotto, 2022. "Finite-Memory Strategies in POMDPs with Long-Run Average Objectives," Mathematics of Operations Research, INFORMS, vol. 47(1), pages 100-119, February.
    • Handle: RePEc:inm:ormoor:v:47:y:2022:i:1:p:100-119
    DOI: 10.1287/moor.2020.1116
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