# Reinforcement Learning for Agile Active Target Sensing with a UAV

arXiv: https://arxiv.org/abs/2212.08214 · HTML: https://mlsanity.com/paper/2212.08214/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 16 Dec 2022.

## Builds on (most cited first, 11 of 11)

- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [An Efficient Sampling-based Method for Online Informative Path Planning in Unknown Environments](https://mlsanity.com/paper/1909.09548/) (2019)
- [Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search](https://mlsanity.com/paper/1902.10182/) (2019)
- [Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing](https://mlsanity.com/paper/2109.13570/) (2021)
- [Search-based Motion Planning for Quadrotors using Linear Quadratic Minimum Time Control](https://mlsanity.com/paper/1709.05401/) (2017)
- [Informative Path Planning for Active Field Mapping under Localization Uncertainty](https://mlsanity.com/paper/1902.09660/) (2019)
- [TIGRIS: An Informed Sampling-based Algorithm for Informative Path Planning](https://mlsanity.com/paper/2203.12830/) (2022)
- [Experiments in Adaptive Replanning for Fast Autonomous Flight in Forests](https://mlsanity.com/paper/2203.01370/) (2022)
- [Online Informative Path Planning for Active Classification on UAVs](https://mlsanity.com/paper/1606.08164/) (2016)
- [Dispersion-Minimizing Motion Primitives for Search-Based Motion Planning](https://mlsanity.com/paper/2103.14603/) (2021)

## Built on by (most cited first, 2 of 2)

- [Active search and coverage using point-cloud reinforcement learning](https://mlsanity.com/paper/2312.11410/) (2023)
- [SEArch: Optimistic Policy Selection Between Scene Noise and Drift for UAV Radar Search](https://mlsanity.com/paper/2606.01325/) (2026)

## Abstract (arXiv)

Active target sensing is the task of discovering and classifying an unknown number of targets in an environment and is critical in search-and-rescue missions. This paper develops a deep reinforcement learning approach to plan informative trajectories that increase the likelihood for an uncrewed aerial vehicle (UAV) to discover missing targets. Our approach efficiently (1) explores the environment to discover new targets, (2) exploits its current belief of the target states and incorporates inaccurate sensor models for high-fidelity classification, and (3) generates dynamically feasible trajectories for an agile UAV by employing a motion primitive library. Extensive simulations on randomly generated environments show that our approach is more efficient in discovering and classifying targets than several other baselines. A unique characteristic of our approach, in contrast to heuristic informative path planning approaches, is that it is robust to varying amounts of deviations of the prior belief from the true target distribution, thereby alleviating the challenge of designing heuristics specific to the application conditions.