A Finite-State Controller Based Offline Solver for Deterministic POMDPs
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Abstract (arXiv)
Deterministic partially observable Markov decision processes (DetPOMDPs) often arise in planning problems where the agent is uncertain about its environmental state but can act and observe deterministically. In this paper, we propose DetMCVI, an adaptation of the Monte Carlo Value Iteration (MCVI) algorithm for DetPOMDPs, which builds policies in the form of finite-state controllers (FSCs). DetMCVI solves large problems with a high success rate, outperforming existing baselines for DetPOMDPs. We also verify the performance of the algorithm in a real-world mobile robot forest mapping scenario.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 8 of its 42 references that are in the map. First posted on arXiv 1 May 2025.
Builds on
8 of 42 references.
References · 8
- 2014Multiple-Environment Markov Decision Processescited by 12
- 2014Optimal Cost Almost-sure Reachability in POMDPscited by 12
- 2016POMDP-lite for Robust Robot Planning under Uncertaintycited by 12
- 2022Inductive Synthesis of Finite-State Controllers for POMDPscited by 8
- 2019Long-Term Robot Navigation in Indoor Environments Estimating Patterns in Traversability Changescited by 6
- 2021On Anderson acceleration for partially observable Markov decision processescited by 3
- 2020LAMP: Learning a Motion Policy to Repeatedly Navigate in an Uncertain Environmentcited by 1
- 2022Stochastic Planning for ASV Navigation Using Satellite Imagescited by 1
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.