The Complexity of Approximating the Value in Revealing POMDPs with Long-Run Average Objectives
Year: 2026
Type: preprint
Abstract: We study partially observable Markov decision processes (POMDPs) with long-run average objectives, where the payoff is defined as the limit inferior of the expected average rewards. We consider the computational problem of approximating the long-run average value of a POMDP. In general, the long-run average value of a POMDP is neither computable nor approximable. We therefore consider the subclass of revealing POMDPs. Informally, a POMDP is revealing when the controller observes the underlying state with positive probability at each stage of the process. Our main contributions are threefold. First, we illustrate the practical relevance of this class of POMDPs through an application in control and optimization. Second, we present an exponential-time algorithm for the value approximation problem. Third, we establish EXPTIME-hardness by a reduction from the problem of almost-sure safety in POMDPs. Together, these results show that the problem of approximating the long-run average value in revealing POMDPs is EXPTIME-complete.
Source: arXiv (Cornell University)
Institution: Institute of Science and Technology Austria
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Austrian Science Fund
OpenAlex ID: W7206150634