Online Planning in POMDPs with State-Requests
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Abstract (arXiv)
In key real-world problems, full state information is sometimes available but only at a high cost, like activating precise yet energy-intensive sensors or consulting humans, thereby compelling the agent to operate under partial observability. For this scenario, we propose AEMS-SR (Anytime Error Minimization Search with State Requests), a principled online planning algorithm tailored for POMDPs with state requests. By representing the search space as a graph instead of a tree, AEMS-SR avoids the exponential growth of the search space originating from state requests. Theoretical analysis demonstrates AEMS-SR's varepsilon-optimality, ensuring solution quality, while empirical evaluations illustrate its effectiveness compared with AEMS and POMCP, two SOTA online planning algorithms. AEMS-SR enables efficient planning in domains characterized by partial observability and costly state requests offering practical benefits across various applications.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 21 references that are in the map. First posted on arXiv 26 Jul 2024.
Builds on
6 of 21 references.
References · 6
- 2018Deep Variational Reinforcement Learning for POMDPscited by 122
- 2016DESPOT: Online POMDP Planning with Regularizationcited by 105
- 2014Online Planning Algorithms for POMDPscited by 42
- 2013Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processescited by 24
- 2020Active Measure Reinforcement Learning for Observation Cost Minimizationcited by 17
- 2023Act-Then-Measure: Reinforcement Learning for Partially Observable Environments with Active Measuringcited by 10
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Citing papers per year: 2025: 1.
1 paper.