# Multi-Agent Deep Reinforcement Learning Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing

arXiv: https://arxiv.org/abs/2009.11277 · HTML: https://mlsanity.com/paper/2009.11277/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2020.

## Built on by (most cited first, 24 of 24)

- [Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey](https://mlsanity.com/paper/2110.13484/) (2021)
- [Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey](https://mlsanity.com/paper/2211.04324/) (2022)
- [Evolutionary Multi-Objective Reinforcement Learning Based Trajectory Control and Task Offloading in UAV-Assisted Mobile Edge Computing](https://mlsanity.com/paper/2202.12028/) (2022)
- [Robustness Testing for Multi-Agent Reinforcement Learning: State Perturbations on Critical Agents](https://mlsanity.com/paper/2306.06136/) (2023)
- [Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics](https://mlsanity.com/paper/2301.00912/) (2023)
- [Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challenges](https://mlsanity.com/paper/2305.10091/) (2023)
- [Scalable UAV Multi-Hop Networking via Multi-Agent Reinforcement Learning with Large Language Models](https://mlsanity.com/paper/2505.08448/) (2025)
- [QTypeMix: Enhancing Multi-Agent Cooperative Strategies through Heterogeneous and Homogeneous Value Decomposition](https://mlsanity.com/paper/2408.07098/) (2024)
- [Cluster-Based Multi-Agent Task Scheduling for Space-Air-Ground Integrated Networks](https://mlsanity.com/paper/2412.10700/) (2024)
- [Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence](https://mlsanity.com/paper/2201.11410/) (2022)
- [Fast and computationally efficient generative adversarial network algorithm for unmanned aerial vehicle-based network coverage optimization](https://mlsanity.com/paper/2203.13607/) (2022)
- [Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach](https://mlsanity.com/paper/2411.14052/) (2024)
- [Multi-Agent DRL for Queue-Aware Task Offloading in Hierarchical MEC-Enabled Air-Ground Networks](https://mlsanity.com/paper/2503.03391/) (2025)
- [Deep Reinforcement Learning-Based Long-Range Autonomous Valet Parking for Smart Cities](https://mlsanity.com/paper/2109.11661/) (2021)
- [Multiscale Adaptive Scheduling and Path-Planning for Power-Constrained UAV-Relays via SMDPs](https://mlsanity.com/paper/2209.07655/) (2022)
- [Personalized Federated Deep Reinforcement Learning-based Trajectory Optimization for Multi-UAV Assisted Edge Computing](https://mlsanity.com/paper/2309.02193/) (2023)
- [Distributed Multi-Objective Dynamic Offloading Scheduling for Air-Ground Cooperative MEC](https://mlsanity.com/paper/2403.10927/) (2024)
- [DeepAir: A Multi-Agent Deep Reinforcement Learning Based Scheme for an Unknown User Location Problem](https://mlsanity.com/paper/2408.05712/) (2024)
- [Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2411.11099/) (2024)
- [Energy Efficient Task Offloading in UAV-Enabled MEC Using a Fully Decentralized Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2508.06863/) (2025)
- [Graph Neural Networks, Deep Reinforcement Learning and Probabilistic Topic Modeling for Strategic Multiagent Settings](https://mlsanity.com/paper/2511.10501/) (2025)
- [AC-MASAC: An Attentive Curriculum Learning Framework for Heterogeneous UAV Swarm Coordination](https://mlsanity.com/paper/2602.11735/) (2026)
- [Scaling up Energy-Aware Multi-Agent Reinforcement Learning for Mission-Oriented Drone Networks with Individual Reward](https://mlsanity.com/paper/2605.24992/) (2026)
- [Threat-Aware Energy-Efficient Deployment for Dynamic UAV Networks: A Multi-Agent RL Approach](https://mlsanity.com/paper/2609.30690/) (2026)

## Abstract (arXiv)

An unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) framework is proposed, where several UAVs having different trajectories fly over the target area and support the user equipments (UEs) on the ground. We aim to jointly optimize the geographical fairness among all the UEs, the fairness of each UAV' UE-load and the overall energy consumption of UEs. The above optimization problem includes both integer and continues variables and it is challenging to solve. To address the above problem, a multi-agent deep reinforcement learning based trajectory control algorithm is proposed for managing the trajectory of each UAV independently, where the popular Multi-Agent Deep Deterministic Policy Gradient (MADDPG) method is applied. Given the UAVs' trajectories, a low-complexity approach is introduced for optimizing the offloading decisions of UEs. We show that our proposed solution has considerable performance over other traditional algorithms, both in terms of the fairness for serving UEs, fairness of UE-load at each UAV and energy consumption for all the UEs.