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

arXiv: https://arxiv.org/abs/2012.03158 · HTML: https://mlsanity.com/paper/2012.03158/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2020.

## Built on by (most cited first, 28 of 28)

- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021)
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- [Spectrum Surveying: Active Radio Map Estimation with Autonomous UAVs](https://mlsanity.com/paper/2201.04125/) (2022)
- [Federated Learning and Meta Learning: Approaches, Applications, and Directions](https://mlsanity.com/paper/2210.13111/) (2022)
- [Distributed Reinforcement Learning for Age of Information Minimization in Real-Time IoT Systems](https://mlsanity.com/paper/2104.01527/) (2021)
- [Optimization of Image Transmission in a Cooperative Semantic Communication Networks](https://mlsanity.com/paper/2301.00433/) (2023)
- [Deep Reinforcement Learning for Trajectory Path Planning and Distributed Inference in Resource-Constrained UAV Swarms](https://mlsanity.com/paper/2212.11201/) (2022)
- [Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless Networks](https://mlsanity.com/paper/2209.13532/) (2022)
- [Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics](https://mlsanity.com/paper/2301.00912/) (2023)
- [Meta-Reinforcement Learning for Reliable Communication in THz/VLC Wireless VR Networks](https://mlsanity.com/paper/2102.12277/) (2021)
- [Continual Meta-Reinforcement Learning for UAV-Aided Vehicular Wireless Networks](https://mlsanity.com/paper/2207.06131/) (2022)
- [Hierarchical Multi-Agent DRL Based Dynamic Cluster Reconfiguration for UAV Mobility Management](https://mlsanity.com/paper/2412.16167/) (2024)
- [Predicting Multi-Antenna Frequency-Selective Channels via Meta-Learned Linear Filters based on Long-Short Term Channel Decomposition](https://mlsanity.com/paper/2203.12715/) (2022)
- [Model-aided Deep Reinforcement Learning for Sample-efficient UAV Trajectory Design in IoT Networks](https://mlsanity.com/paper/2104.10403/) (2021)
- [Muti-Agent Proximal Policy Optimization For Data Freshness in UAV-assisted Networks](https://mlsanity.com/paper/2303.08680/) (2023)
- [Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification](https://mlsanity.com/paper/2409.12978/) (2024)
- [Learning from Images: Proactive Caching with Parallel Convolutional Neural Networks](https://mlsanity.com/paper/2108.06817/) (2021)
- [Responsive Regulation of Dynamic UAV Communication Networks Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2108.11012/) (2021)
- [Online Bayesian Meta-Learning for Cognitive Tracking Radar](https://mlsanity.com/paper/2207.06917/) (2022)
- [Collaborative Reinforcement Learning Based Unmanned Aerial Vehicle (UAV) Trajectory Design for 3D UAV Tracking](https://mlsanity.com/paper/2401.12079/) (2024)
- [State-of-the-art in Robot Learning for Multi-Robot Collaboration: A Comprehensive Survey](https://mlsanity.com/paper/2408.11822/) (2024)
- [Maximizing User Connectivity in AI-Enabled Multi-UAV Networks: A Distributed Strategy Generalized to Arbitrary User Distributions](https://mlsanity.com/paper/2411.05205/) (2024)
- [Partially Decentralized Multi-Agent Q-Learning via Digital Cousins for Wireless Networks](https://mlsanity.com/paper/2503.05970/) (2025)
- [Generalizable Agent Modeling for Agent Collaboration-Competition Adaptation with Multi-Retrieval and Dynamic Generation](https://mlsanity.com/paper/2506.16718/) (2025)
- [Meta-Learning-Based Handover Management in NextG O-RAN](https://mlsanity.com/paper/2512.22022/) (2025)
- [Optimizing Energy and Data Collection in UAV-aided IoT Networks using Attention-based Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2601.14092/) (2026)
- [Adaptive Machine Learning Framework for UAV Trajectory Optimization in O-RAN](https://mlsanity.com/paper/2606.24483/) (2026)

## Abstract (arXiv)

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 (VDRL) 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 local 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 meta-learning improves the convergence speed of the VD-RL algorithm by up to 53.8% when the DBSs must deal with a previously unseen task.