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arXiv · 2605.07537

Multi-Environment POMDPs with Finite-Horizon Objectives

Léonard Brice·Filip Cano·Krishnendu Chatterjee·Thomas A. Henzinger·Stefanie Muroya
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Abstract

Partially Observable Markov Decision Processes (POMDPs) are systems in which one agent interacts with a stochastic environment, and receives only partial information about the current state. In a multi-environment POMDP (MEPOMDP), the initial state is unknown, and assumed to be adversarially chosen. In this work we focus on computing the optimal value and policy in MEPOMDPs with finite-horizon objectives. That problem is known to be PSPACE-complete in POMDPs. Our main results are as follows: (1) we establish that it is also PSPACE-complete in the more general setting of MEPOMDPs; (2) we present a practical algorithm and evaluate it on classical benchmarks, significantly outperforming the only previously known algorithm.

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Léonard Brice, Filip Cano, Krishnendu Chatterjee, Thomas A. Henzinger, Stefanie Muroya. 2026-05-08. Multi-Environment POMDPs with Finite-Horizon Objectives. https://arxiv.org/abs/2605.07537

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