# Online Active Perception for Partially Observable Markov Decision Processes with Limited Budget

arXiv: https://arxiv.org/abs/1910.02130 · HTML: https://mlsanity.com/paper/1910.02130/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2019.

## Built on by (most cited first, 4 of 4)

- [Act-Then-Measure: Reinforcement Learning for Partially Observable Environments with Active Measuring](https://mlsanity.com/paper/2303.08271/) (2023)
- [Submodular Information Selection for Hypothesis Testing with Misclassification Penalties](https://mlsanity.com/paper/2405.10930/) (2024)
- [Simultaneous Perception-Action Design via Invariant Finite Belief Sets](https://mlsanity.com/paper/2109.05073/) (2021)
- [Robust Information Selection for Hypothesis Testing with Misclassification Penalties](https://mlsanity.com/paper/2502.14738/) (2025)

## Abstract (arXiv)

Active perception strategies enable an agent to selectively gather information in a way to improve its performance. In applications in which the agent does not have prior knowledge about the available information sources, it is crucial to synthesize active perception strategies at runtime. We consider a setting in which at runtime an agent is capable of gathering information under a limited budget. We pose the problem in the context of partially observable Markov decision processes. We propose a generalized greedy strategy that selects a subset of information sources with near-optimality guarantees on uncertainty reduction. Our theoretical analysis establishes that the proposed active perception strategy achieves near-optimal performance in terms of expected cumulative reward. We demonstrate the resulting strategies in simulations on a robotic navigation problem.