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Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

arXiv 1810.07862 ↗First posted 18 Oct 2018cs.NI cs.LGcited by 167

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Cited by 30 in Deep reinforcement learning methods, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

This paper presents a comprehensive literature review on applications of deep reinforcement learning in communications and networking. Modern networks, e.g., Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and large-scale networks, the state and action spaces are usually large, and the reinforcement learning may not be able to find the optimal policy in reasonable time. Therefore, deep reinforcement learning, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings. In this survey, we first give a tutorial of deep reinforcement learning from fundamental concepts to advanced models. Then, we review deep reinforcement learning approaches proposed to address emerging issues in communications and networking. The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks such as 5G and beyond. Furthermore, we present applications of deep reinforcement learning for traffic routing, resource sharing, and data collection. Finally, we highlight important challenges, open issues, and future research directions of applying deep reinforcement learning.

Key facts. Cited by 167 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Oct 2018.

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Citing papers per year: 2021: 51; 2022: 24; 2023: 22; 2024: 30; 2025: 23; 2026: 17.

167 papers.

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  1. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 129
  2. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 97
  3. 2023Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimizationcited by 56
  4. 2025Towards General-Purpose Model-Free Reinforcement Learningcited by 41
  5. 2021Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Surveycited by 34
  6. 2023Multi-agent Reinforcement Learning: A Comprehensive Surveycited by 33
  7. 2025Towards Better Alignment: Training Diffusion Models with Reinforcement Learning Against Sparse Rewardscited by 32
  8. 2021Ensemble Bootstrapping for Q-Learningcited by 31
  9. 2021Fusion of Federated Learning and Industrial Internet of Things: A Surveycited by 29
  10. 2021Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Surveycited by 28
  11. 2022Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Surveycited by 28
  12. 2023Formally Explaining Neural Networks within Reactive Systemscited by 24
38 more
  1. 2021Transfer Learning for Future Wireless Networks: A Comprehensive Surveycited by 18
  2. 2023Machine Learning for Large-Scale Optimization in 6G Wireless Networkscited by 18
  3. 2021A Survey on Deep Reinforcement Learning for Audio-Based Applicationscited by 17
  4. 2022A Temporal-Pattern Backdoor Attack to Deep Reinforcement Learningcited by 16
  5. 2021Reinforcement Learning-powered Semantic Communication via Semantic Similaritycited by 15
  6. 2021Safe Online Convex Optimization with Unknown Linear Safety Constraintscited by 13
  7. 2021Robust Android Malware Detection System against Adversarial Attacks using Q-Learningcited by 12
  8. 2024Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approachcited by 12
  9. 2021Multi-hop RIS-Empowered Terahertz Communications: A DRL-based Hybrid Beamforming Designcited by 10
  10. 2021Sample Complexity and Overparameterization Bounds for Temporal Difference Learning with Neural Network Approximationcited by 10
  11. 2021A Survey on Deep Reinforcement Learning for Data Processing and Analyticscited by 10
  12. 2024Conservative and Risk-Aware Offline Multi-Agent Reinforcement Learningcited by 10
  13. 2024Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Surveycited by 10
  14. 2024Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectoriescited by 9
  15. 2024Applications of Generative AI (GAI) for Mobile and Wireless Networking: A Surveycited by 9
  16. 2021Self-play Learning Strategies for Resource Assignment in Open-RAN Networkscited by 8
  17. 2021Full Gradient DQN Reinforcement Learning: A Provably Convergent Schemecited by 8
  18. 2021Deep Reinforcement Learning for Simultaneous Sensing and Channel Access in Cognitive Networkscited by 8
  19. 2022Deep reinforcement learning under signal temporal logic constraints using Lagrangian relaxationcited by 8
  20. 2022Machine Learning in NextG Networks via Generative Adversarial Networkscited by 8
  21. 2022Deep Learning Methods for Fingerprint-Based Indoor Positioning: A Reviewcited by 8
  22. 2024Acceleration for Deep Reinforcement Learning using Parallel and Distributed Computing: A Surveycited by 8
  23. 2021Discrete-Time Mean Field Control with Environment Statescited by 7
  24. 2021Machine Learning for Security in Vehicular Networks: A Comprehensive Surveycited by 7
  25. 2021Cooperative Multi-Agent Reinforcement Learning Based Distributed Dynamic Spectrum Access in Cognitive Radio Networkscited by 7
  26. 2022New Challenges in Reinforcement Learning: A Survey of Security and Privacycited by 7
  27. 2026Agentic AI Empowered Intent-Based Networking for 6Gcited by 7
  28. 2022Reinforcement Learning in Practice: Opportunities and Challengescited by 6
  29. 2021Joint Resource Management for MC-NOMA: A Deep Reinforcement Learning Approachcited by 5
  30. 2023AccDecoder: Accelerated Decoding for Neural-enhanced Video Analyticscited by 5
  31. 2024Stochastic Q-learning for Large Discrete Action Spacescited by 5
  32. 2025An Offline Multi-Agent Reinforcement Learning Framework for Radio Resource Managementcited by 5
  33. 2021Dynamic Multichannel Access via Multi-agent Reinforcement Learning: Throughput and Fairness Guaranteescited by 4
  34. 2022MR-iNet Gym: Framework for Edge Deployment of Deep Reinforcement Learning on Embedded Software Defined Radiocited by 4
  35. 2023Mutation Testing of Deep Reinforcement Learning Based on Real Faultscited by 4
  36. 2023Dynamic Regret Analysis of Safe Distributed Online Optimization for Convex and Non-convex Problemscited by 4
  37. 2023Handover Protocol Learning for LEO Satellite Networks: Access Delay and Collision Minimizationcited by 4
  38. 2024Multiple Access in the Era of Distributed Computing and Edge Intelligencecited by 4

50 most cited of 167 All 167 (JSON)