# Continual Meta-Reinforcement Learning for UAV-Aided Vehicular Wireless Networks

arXiv: https://arxiv.org/abs/2207.06131 · HTML: https://mlsanity.com/paper/2207.06131/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 37 references that are in the map.
- First posted on arXiv 13 Jul 2022.

## Builds on (most cited first, 10 of 10)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [A Tutorial on 5G NR V2X Communications](https://mlsanity.com/paper/2102.04538/) (2021)
- [Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks](https://mlsanity.com/paper/2012.03158/) (2020)
- [Guided Meta-Policy Search](https://mlsanity.com/paper/1904.00956/) (2019)
- [CoMPS: Continual Meta Policy Search](https://mlsanity.com/paper/2112.04467/) (2021)
- [Meta-Reinforcement Learning for Trajectory Design in Wireless UAV Networks](https://mlsanity.com/paper/2005.12394/) (2020)
- [UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2007.00544/) (2020)
- [Mobile Edge Computing via a UAV-Mounted Cloudlet: Optimization of Bit Allocation and Path Planning](https://mlsanity.com/paper/1609.05362/) (2016)
- [UAV Path Planning using Global and Local Map Information with Deep Reinforcement Learning](https://mlsanity.com/paper/2010.06917/) (2020)
- [Autonomous UAV Base Stations for Next Generation Wireless Networks: A Deep Learning Approach](https://mlsanity.com/paper/2107.13869/) (2021)

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

- [Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks](https://mlsanity.com/paper/2501.14603/) (2025)
- [Density-Aware Reinforcement Learning to Optimise Energy Efficiency in UAV-Assisted Networks](https://mlsanity.com/paper/2306.08785/) (2023)
- [MADRL-based UAVs Trajectory Design with Anti-Collision Mechanism in Vehicular Networks](https://mlsanity.com/paper/2402.03342/) (2024)

## Abstract (arXiv)

Unmanned aerial base stations (UABSs) can be deployed in vehicular wireless networks to support applications such as extended sensing via vehicle-to-everything (V2X) services. A key problem in such systems is designing algorithms that can efficiently optimize the trajectory of the UABS in order to maximize coverage. In existing solutions, such optimization is carried out from scratch for any new traffic configuration, often by means of conventional reinforcement learning (RL). In this paper, we propose the use of continual meta-RL as a means to transfer information from previously experienced traffic configurations to new conditions, with the goal of reducing the time needed to optimize the UABS's policy. Adopting the Continual Meta Policy Search (CoMPS) strategy, we demonstrate significant efficiency gains as compared to conventional RL, as well as to naive transfer learning methods.