# Learning with Dynamics: Autonomous Regulation of UAV Based Communication Networks with Dynamic UAV Crew

arXiv: https://arxiv.org/abs/2409.17139 · HTML: https://mlsanity.com/paper/2409.17139/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 25 Sep 2024.

## Builds on (most cited first, 5 of 5)

- [Cooperative and Distributed Reinforcement Learning of Drones for Field Coverage](https://mlsanity.com/paper/1803.07250/) (2018)
- [Deep Q-Learning for Nash Equilibria: Nash-DQN](https://mlsanity.com/paper/1904.10554/) (2019)
- [When Learning Meets Dynamics: Distributed User Connectivity Maximization in UAV-Based Communication Networks](https://mlsanity.com/paper/2409.06010/) (2024)
- [Achieving Correlated Equilibrium by Studying Opponent's Behavior Through Policy-Based Deep Reinforcement Learning](https://mlsanity.com/paper/2004.08523/) (2020)
- [Optimal Charging Profile Design for Solar-Powered Sustainable UAV Communication Networks](https://mlsanity.com/paper/2302.06092/) (2023)

## Abstract (arXiv)

Unmanned Aerial Vehicle (UAV) based communication networks (UCNs) are a key component in future mobile networking. To handle the dynamic environments in UCNs, reinforcement learning (RL) has been a promising solution attributed to its strong capability of adaptive decision-making free of the environment models. However, most existing RL-based research focus on control strategy design assuming a fixed set of UAVs. Few works have investigated how UCNs should be adaptively regulated when the serving UAVs change dynamically. This article discusses RL-based strategy design for adaptive UCN regulation given a dynamic UAV set, addressing both reactive strategies in general UCNs and proactive strategies in solar-powered UCNs. An overview of the UCN and the RL framework is first provided. Potential research directions with key challenges and possible solutions are then elaborated. Some of our recent works are presented as case studies to inspire innovative ways to handle dynamic UAV crew with different RL algorithms.