mlsanityData to 14 Oct 2026

Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectories

arXiv 2404.14238 ↗First posted 22 Apr 2024cs.NI cs.AIcited by 9

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Mobile Edge Computing (MEC) broadens the scope of computation and storage beyond the central network, incorporating edge nodes close to end devices. This expansion facilitates the implementation of large-scale "connected things" within edge networks. The advent of applications necessitating real-time, high-quality service presents several challenges, such as low latency, high data rate, reliability, efficiency, and security, all of which demand resolution. The incorporation of reinforcement learning (RL) methodologies within MEC networks promotes a deeper understanding of mobile user behaviors and network dynamics, thereby optimizing resource use in computing and communication processes. This paper offers an exhaustive survey of RL applications in MEC networks, initially presenting an overview of RL from its fundamental principles to the latest advanced frameworks. Furthermore, it outlines various RL strategies employed in offloading, caching, and communication within MEC networks. Finally, it explores open issues linked with software and hardware platforms, representation, RL robustness, safe RL, large-scale scheduling, generalization, security, and privacy. The paper proposes specific RL techniques to mitigate these issues and provides insights into their practical applications.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 94 of its 240 references that are in the map. First posted on arXiv 22 Apr 2024.

Builds on

94 of 240 references.

References · 94

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38 more
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50 most cited of 94 All 94 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 6; 2026: 3.

9 papers.

  1. 2025Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Surveycited by 14
  2. 2025A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networkscited by 3
  3. 2025Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehiclescited by 1
  4. 2025ReaCritic: Reasoning Transformer-based DRL Critic-model Scaling For Wireless Networkscited by 1
  5. 2025Optimistic Learning for Communication Networkscited by 0
  6. 2025HCF: Hierarchical Cascade Framework for Distributed Multi-Stage Image Compressioncited by 0
  7. 2026WINFlowNets: Warm-up Integrated Networks Training of Generative Flow Networks for Robotics and Machine Fault Adaptationcited by 0
  8. 2026Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directionscited by 0
  9. 2026Learning-Based Collaborative MEC for LLM Inference with Soft-Deadline Awareness via Transformer-Enhanced PPOcited by 0

Similar, not cited

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  1. 2025RL-based Adaptive Task Offloading in Mobile-Edge Computing for Future IoT Networks ↗
  2. 2025A Novel Deep Reinforcement Learning Method for Computation Offloading in Multi-User Mobile Edge Computing with Decentralization
  3. 2023Multi-objective Deep Reinforcement Learning for Mobile Edge Computing
  4. 2025Generalizable Pareto-Optimal Offloading with Reinforcement Learning in Mobile Edge Computing
  5. 2025Cooperative Task Offloading through Asynchronous Deep Reinforcement Learning in Mobile Edge Computing for Future Networks
  6. 2024To Train or Not to Train: Balancing Efficiency and Training Cost in Deep Reinforcement Learning for Mobile Edge Computing
  7. 2024dRG-MEC: Decentralized Reinforced Green Offloading for MEC-enabled Cloud Network
  8. 2024Latency-Aware Resource Allocation for Mobile Edge Generation and Computing via Deep Reinforcement Learning