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

arXiv 2009.11277 ↗First posted 23 Sep 2020eess.SP cs.LGcited by 22

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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.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Sep 2020.

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Citing papers per year: 2021: 2; 2022: 5; 2023: 4; 2024: 5; 2025: 4; 2026: 2.

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  1. 2021Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Surveycited by 34
  2. 2022Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Surveycited by 27
  3. 2022Evolutionary Multi-Objective Reinforcement Learning Based Trajectory Control and Task Offloading in UAV-Assisted Mobile Edge Computingcited by 10
  4. 2023Robustness Testing for Multi-Agent Reinforcement Learning: State Perturbations on Critical Agentscited by 7
  5. 2023Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semanticscited by 5
  6. 2023Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challengescited by 5
  7. 2025Scalable UAV Multi-Hop Networking via Multi-Agent Reinforcement Learning with Large Language Modelscited by 4
  8. 2024QTypeMix: Enhancing Multi-Agent Cooperative Strategies through Heterogeneous and Homogeneous Value Decompositioncited by 3
  9. 2024Cluster-Based Multi-Agent Task Scheduling for Space-Air-Ground Integrated Networkscited by 3
  10. 2022Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligencecited by 2
  11. 2022Fast and computationally efficient generative adversarial network algorithm for unmanned aerial vehicle-based network coverage optimizationcited by 2
  12. 2021Deep Reinforcement Learning-Based Long-Range Autonomous Valet Parking for Smart Citiescited by 0
10 more
  1. 2022Multiscale Adaptive Scheduling and Path-Planning for Power-Constrained UAV-Relays via SMDPscited by 0
  2. 2023Personalized Federated Deep Reinforcement Learning-based Trajectory Optimization for Multi-UAV Assisted Edge Computingcited by 0
  3. 2024Distributed Multi-Objective Dynamic Offloading Scheduling for Air-Ground Cooperative MECcited by 0
  4. 2024DeepAir: A Multi-Agent Deep Reinforcement Learning Based Scheme for an Unknown User Location Problemcited by 0
  5. 2024Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learningcited by 0
  6. 2025Multi-Agent DRL for Queue-Aware Task Offloading in Hierarchical MEC-Enabled Air-Ground Networkscited by 0
  7. 2025Energy Efficient Task Offloading in UAV-Enabled MEC Using a Fully Decentralized Deep Reinforcement Learning Approachcited by 0
  8. 2025Graph Neural Networks, Deep Reinforcement Learning and Probabilistic Topic Modeling for Strategic Multiagent Settingscited by 0
  9. 2026Scaling up Energy-Aware Multi-Agent Reinforcement Learning for Mission-Oriented Drone Networks with Individual Rewardcited by 0
  10. 2026Threat-Aware Energy-Efficient Deployment for Dynamic UAV Networks: A Multi-Agent RL Approachcited by 0