# Deep Reinforcement Learning-Based Approach for a Single Vehicle Persistent Surveillance Problem with Fuel Constraints

arXiv: https://arxiv.org/abs/2404.06423 · HTML: https://mlsanity.com/paper/2404.06423/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 9 Apr 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

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

- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [A Closer Look at Invalid Action Masking in Policy Gradient Algorithms](https://mlsanity.com/paper/2006.14171/) (2020)
- [Algorithms for Routing an Unmanned Aerial Vehicle in the presence of Refueling Depots](https://mlsanity.com/paper/1304.0494/) (2013)
- [Multi-Agent Deep Reinforcement Learning For Persistent Monitoring With Sensing, Communication, and Localization Constraints](https://mlsanity.com/paper/2109.06831/) (2021)
- [Formulations and algorithms for the multiple depot, fuel-constrained, multiple vehicle routing problem](https://mlsanity.com/paper/1508.05968/) (2015)
- [Persistent Monitoring of Events with Stochastic Arrivals at Multiple Stations](https://mlsanity.com/paper/1309.6041/) (2013)

## Abstract (arXiv)

This article presents a deep reinforcement learning-based approach to tackle a persistent surveillance mission requiring a single unmanned aerial vehicle initially stationed at a depot with fuel or time-of-flight constraints to repeatedly visit a set of targets with equal priority. Owing to the vehicle's fuel or time-of-flight constraints, the vehicle must be regularly refueled, or its battery must be recharged at the depot. The objective of the problem is to determine an optimal sequence of visits to the targets that minimizes the maximum time elapsed between successive visits to any target while ensuring that the vehicle never runs out of fuel or charge. We present a deep reinforcement learning algorithm to solve this problem and present the results of numerical experiments that corroborate the effectiveness of this approach in comparison with common-sense greedy heuristics.