# Quality of service based radar resource management using deep reinforcement learning

arXiv: https://arxiv.org/abs/2010.10210 · HTML: https://mlsanity.com/paper/2010.10210/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [Multi-Objective Reinforcement Learning for Cognitive Radar Resource Management](https://mlsanity.com/paper/2506.20853/) (2025)
- [Explainable AI for Radar Resource Management: Modified LIME in Deep Reinforcement Learning](https://mlsanity.com/paper/2506.20916/) (2025)
- [Constrained Deep Reinforcement Learning for Cognitive Radar Resource Management](https://mlsanity.com/paper/2606.05526/) (2026)

## Abstract (arXiv)

An intelligent radar resource management is an essential milestone in the development of a cognitive radar system. The quality of service based resource allocation model (Q-RAM) is a framework allowing for intelligent decision making but classical solutions seem insufficient for real-time application in a modern radar system. In this paper, we present a solution for the Q-RAM radar resource management problem using deep reinforcement learning considerably improving on runtime performance.