Online Planning in POMDPs with State-Requests
<|reference_start|>Online Planning in POMDPs with State-Requests: 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.<|reference_end|>
@article{avalos2024online,
    title={Online Planning in POMDPs with State-Requests},
    author={Raphael Avalos, Eugenio Bargiacchi, Ann Now'e, Diederik M. Roijers,
  Frans A. Oliehoek},
    journal={Reinforcement Learning Journal, vol. 1, no. 1, 2024, pp. TBD},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.18812},
    primaryClass={cs.LG cs.AI}
}