# Online Planning in POMDPs with State-Requests

arXiv: https://arxiv.org/abs/2407.18812 · HTML: https://mlsanity.com/paper/2407.18812/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 26 Jul 2024.

## Builds on (most cited first, 6 of 6)

- [Deep Variational Reinforcement Learning for POMDPs](https://mlsanity.com/paper/1806.02426/) (2018)
- [DESPOT: Online POMDP Planning with Regularization](https://mlsanity.com/paper/1609.03250/) (2016)
- [Online Planning Algorithms for POMDPs](https://mlsanity.com/paper/1401.3436/) (2014)
- [Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes](https://mlsanity.com/paper/1302.1525/) (2013)
- [Active Measure Reinforcement Learning for Observation Cost Minimization](https://mlsanity.com/paper/2005.12697/) (2020)
- [Act-Then-Measure: Reinforcement Learning for Partially Observable Environments with Active Measuring](https://mlsanity.com/paper/2303.08271/) (2023)

## Built on by (most cited first, 1 of 1)

- [Active Measuring in Reinforcement Learning With Delayed Negative Effects](https://mlsanity.com/paper/2510.14315/) (2025)

## 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.