mlsanityData to 7 Oct 2026

Deep progressive reinforcement learning-based flexible resource scheduling framework for IRS and UAV-assisted MEC system

arXiv 2408.01248 ↗First posted 2 Aug 2024cs.LG cs.AIcited by 5

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Abstract (arXiv)

The intelligent reflection surface (IRS) and unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is widely used in temporary and emergency scenarios. Our goal is to minimize the energy consumption of the MEC system by jointly optimizing UAV locations, IRS phase shift, task offloading, and resource allocation with a variable number of UAVs. To this end, we propose a Flexible REsource Scheduling (FRES) framework by employing a novel deep progressive reinforcement learning which includes the following innovations: Firstly, a novel multi-task agent is presented to deal with the mixed integer nonlinear programming (MINLP) problem. The multi-task agent has two output heads designed for different tasks, in which a classified head is employed to make offloading decisions with integer variables while a fitting head is applied to solve resource allocation with continuous variables. Secondly, a progressive scheduler is introduced to adapt the agent to the varying number of UAVs by progressively adjusting a part of neurons in the agent. This structure can naturally accumulate experiences and be immune to catastrophic forgetting. Finally, a light taboo search (LTS) is introduced to enhance the global search of the FRES. The numerical results demonstrate the superiority of the FRES framework which can make real-time and optimal resource scheduling even in dynamic MEC systems.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 14 of its 43 references that are in the map. First posted on arXiv 2 Aug 2024.

Builds on

14 of 43 references.

References · 14

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  2. 2016Progressive Neural Networkscited by 1,332
  3. 2015Deep Reinforcement Learning in Large Discrete Action Spacescited by 158
  4. 2023Large AI Model-Based Semantic Communicationscited by 35
  5. 2020Efficient Architecture Search for Continual Learningcited by 34
  6. 2019Deep Reinforcement Learning Based Dynamic Trajectory Control for UAV-assisted Mobile Edge Computingcited by 16
  7. 2020Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimizationcited by 13
  8. 2020Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networkscited by 12
  9. 2020Stacked Auto Encoder Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networkscited by 7
  10. 2019Deep Learning Based Joint Resource Scheduling Algorithms for Hybrid MEC Networkscited by 6
  11. 2020Distributed Resource Scheduling for Large-Scale MEC Systems: A Multi-Agent Ensemble Deep Reinforcement Learning with Imitation Accelerationcited by 5
  12. 2020Joint Trajectory and Passive Beamforming Design for Intelligent Reflecting Surface-Aided UAV Communications: A Deep Reinforcement Learning Approachcited by 4
2 more
  1. 2020UAV-Assisted Intelligent Reflecting Surface Symbiotic Radio Systemcited by 4
  2. 2022Joint Optimization of Deployment and Trajectory in UAV and IRS-Assisted IoT Data Collection Systemcited by 2

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Cited by year

Citing papers per year: 2024: 3; 2025: 2.

5 papers.

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  3. 2024Predictive Covert Communication Against Multi-UAV Surveillance Using Graph Koopman Autoencodercited by 1
  4. 2025Attention-based UAV Trajectory Optimization for Wireless Power Transfer-assisted IoT Systemscited by 1
  5. 2025Towards Intelligent Transportation with Pedestrians and Vehicles In-the-Loop: A Surveillance Video-Assisted Federated Digital Twin Frameworkcited by 1