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Online Planning in POMDPs with State-Requests

Raphaël Avalos, Eugenio Bargiacchi, Ann Nowe, Diederik Roijers, and Frans A Oliehoek. Online Planning in POMDPs with State-Requests. In Proceedings of the First International Conference on Reinforcement Learning (RLC), August 2024.

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Abstract

In key real-world problems, full state information is sometimes available but onlyat a high cost, like activating precise yet energy-intensive sensors or consulting hu-mans, thereby compelling the agent to operate under partial observability. For thisscenario, we propose AEMS-SR (Anytime Error Minimization Search with StateRequests), a principled online planning algorithm tailored for POMDPs with staterequests. By representing the search space as a graph instead of a tree, AEMS-SRavoids the exponential growth of the search space originating from state requests.Theoretical analysis demonstrates AEMS-SR’s ε-optimality, ensuring solution qual-ity, while empirical evaluations illustrate its effectiveness compared with AEMSand POMCP, two SOTA online planning algorithms. AEMS-SR enables efficientplanning in domains characterized by partial observability and costly state requestsoffering practical benefits across various applications.

BibTeX Entry

@inproceedings{Avalos24RLC,
    title=      {Online Planning in {POMDP}s with State-Requests},
    author =    {Rapha\"{e}l Avalos and Eugenio Bargiacchi and Ann Nowe and Diederik Roijers and Frans A Oliehoek},
    booktitle = RLC24,
    year=       2024,
    month =     aug,
    url=        {https://openreview.net/forum?id=E3R1wiANkG}
    keywords =   {refereed},
    abstract={
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 hu-
mans, 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 ε-optimality, ensuring solution qual-
ity, 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.        
    }
}

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