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arXiv · 1904.13360

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

Krishnendu Chatterjee·Raimundo Saona·Bruno Ziliotto
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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.

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Krishnendu Chatterjee, Raimundo Saona, Bruno Ziliotto. 2019-04-30. Finite-Memory Strategies in POMDPs with Long-Run Average Objectives. https://doi.org/10.1287/moor.2020.1116

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