mlsanityData to 14 Oct 2026

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

arXiv 2207.06131 ↗First posted 13 Jul 2022cs.LG cs.NIcited by 3

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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.

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

10 of 37 references.

References · 10

  1. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  2. 2021A Tutorial on 5G NR V2X Communicationscited by 29
  3. 2020Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networkscited by 28
  4. 2019Guided Meta-Policy Searchcited by 26
  5. 2021CoMPS: Continual Meta Policy Searchcited by 15
  6. 2020Meta-Reinforcement Learning for Trajectory Design in Wireless UAV Networkscited by 8
  7. 2020UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning Approachcited by 8
  8. 2016Mobile Edge Computing via a UAV-Mounted Cloudlet: Optimization of Bit Allocation and Path Planningcited by 6
  9. 2020UAV Path Planning using Global and Local Map Information with Deep Reinforcement Learningcited by 4
  10. 2021Autonomous UAV Base Stations for Next Generation Wireless Networks: A Deep Learning Approachcited by 1

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Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 1.

3 papers.

  1. 2025Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networkscited by 2
  2. 2023Density-Aware Reinforcement Learning to Optimise Energy Efficiency in UAV-Assisted Networkscited by 0
  3. 2024MADRL-based UAVs Trajectory Design with Anti-Collision Mechanism in Vehicular Networkscited by 0