Distributed Multi-Agent Meta Learning for Trajectory Design in Wireless Drone Networks

Ye Hu, Mingzhe Chen, Walid Saad, H. Vincent Poor, Shuguang Cui

Research output: Contribution to journalArticlepeer-review

72 Scopus citations


In this paper, the problem of the trajectory design for a group of energy-constrained drones operating in dynamic wireless network environments is studied. In the considered model, a team of drone base stations (DBSs) is dispatched to cooperatively serve clusters of ground users that have dynamic and unpredictable uplink access demands. In this scenario, the DBSs must cooperatively navigate in the considered area to maximize coverage of the dynamic requests of the ground users. This trajectory design problem is posed as an optimization framework whose goal is to find optimal trajectories that maximize the fraction of users served by all DBSs. To find an optimal solution for this non-convex optimization problem under unpredictable environments, a value decomposition based reinforcement learning (VD-RL) solution coupled with a meta-training mechanism is proposed. This algorithm allows the DBSs to dynamically learn their trajectories while generalizing their learning to unseen environments. Analytical results show that, the proposed VD-RL algorithm is guaranteed to converge to a locally optimal solution of the non-convex optimization problem. Simulation results show that, even without meta-training, the proposed VD-RL algorithm can achieve a 53.2% improvement of the service coverage and a 30.6% improvement in terms of the convergence speed, compared to baseline multi-agent algorithms. Meanwhile, the use of the meta-training mechanism improves the convergence speed of the VD-RL algorithm by up to 53.8% when the DBSs must deal with a previously unseen task.

Original languageEnglish (US)
Pages (from-to)3177-3192
Number of pages16
JournalIEEE Journal on Selected Areas in Communications
Issue number10
StatePublished - Oct 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Electrical and Electronic Engineering


  • Drones
  • meta-learning
  • multi-agent reinforcement learning
  • network optimization


Dive into the research topics of 'Distributed Multi-Agent Meta Learning for Trajectory Design in Wireless Drone Networks'. Together they form a unique fingerprint.

Cite this