mlsanityData to 7 Oct 2026

Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial

arXiv 2011.03615 ↗First posted 6 Nov 2020cs.LG cs.NIcited by 59

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Abstract (arXiv)

Deep Reinforcement Learning (DRL) has recently witnessed significant advances that have led to multiple successes in solving sequential decision-making problems in various domains, particularly in wireless communications. The future sixth-generation (6G) networks are expected to provide scalable, low-latency, ultra-reliable services empowered by the application of data-driven Artificial Intelligence (AI). The key enabling technologies of future 6G networks, such as intelligent meta-surfaces, aerial networks, and AI at the edge, involve more than one agent which motivates the importance of multi-agent learning techniques. Furthermore, cooperation is central to establishing self-organizing, self-sustaining, and decentralized networks. In this context, this tutorial focuses on the role of DRL with an emphasis on deep Multi-Agent Reinforcement Learning (MARL) for AI-enabled 6G networks. The first part of this paper will present a clear overview of the mathematical frameworks for single-agent RL and MARL. The main idea of this work is to motivate the application of RL beyond the model-free perspective which was extensively adopted in recent years. Thus, we provide a selective description of RL algorithms such as Model-Based RL (MBRL) and cooperative MARL and we highlight their potential applications in 6G wireless networks. Finally, we overview the state-of-the-art of MARL in fields such as Mobile Edge Computing (MEC), Unmanned Aerial Vehicles (UAV) networks, and cell-free massive MIMO, and identify promising future research directions. We expect this tutorial to stimulate more research endeavors to build scalable and decentralized systems based on MARL.

Key facts. Cited by 59 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Nov 2020.

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Citing papers per year: 2021: 7; 2022: 8; 2023: 7; 2024: 13; 2025: 16; 2026: 8.

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  1. 2025AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challengescited by 167
  2. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
  3. 2023Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimizationcited by 56
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  6. 2021AoI-Aware Resource Allocation for Platoon-Based C-V2X Networks via Multi-Agent Multi-Task Reinforcement Learningcited by 17
  7. 2023Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networkscited by 16
  8. 2025Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Surveycited by 14
  9. 2024Toward Enhanced Reinforcement Learning-Based Resource Management via Digital Twin: Opportunities, Applications, and Challengescited by 10
  10. 2024Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectoriescited by 9
  11. 2022Intelligent Computing: The Latest Advances, Challenges and Futurecited by 6
  12. 2023Safe and Accelerated Deep Reinforcement Learning-based O-RAN Slicing: A Hybrid Transfer Learning Approachcited by 5
38 more
  1. 2023Digital Twin Assisted Deep Reinforcement Learning for Online Admission Control in Sliced Networkcited by 5
  2. 2024Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learningcited by 5
  3. 2022On the Near-Optimality of Local Policies in Large Cooperative Multi-Agent Reinforcement Learningcited by 4
  4. 2022Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless Networkscited by 4
  5. 2024Large Vision Model-Enhanced Digital Twin with Deep Reinforcement Learning for User Association and Load Balancing in Dynamic Wireless Networkscited by 4
  6. 2021Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Surveycited by 3
  7. 2022Deep Reinforcement Learning for Data-Driven Adaptive Scanning in Ptychographycited by 3
  8. 2022UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithmscited by 3
  9. 2025Joint Computation Offloading and Resource Allocation for Uncertain Maritime MEC via Cooperation of UAVs and Vesselscited by 3
  10. 2025Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunitiescited by 3
  11. 2022Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligencecited by 2
  12. 2022Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directionscited by 2
  13. 2024Map2Traj: Street Map Piloted Zero-shot Trajectory Generation with Diffusion Modelcited by 2
  14. 2025MetaSTNet: Multimodal Meta-learning for Cellular Traffic Conformal Predictioncited by 2
  15. 2025Continuous-Time Value Iteration for Multi-Agent Reinforcement Learningcited by 2
  16. 2025AURA: Adaptive Unified Reasoning and Automation with LLM-Guided MARL for NextG Cellular Networkscited by 2
  17. 2022Deep Reinforcement Learning for Power Control in Next-Generation WiFi Network Systemscited by 1
  18. 2023Intelligent multicast routing method based on multi-agent deep reinforcement learning in SDWNcited by 1
  19. 2023Learning-Augmented Decentralized Online Convex Optimization in Networkscited by 1
  20. 2024Generative AI for O-RAN Slicing: A Semi-Supervised Approach with VAE and Contrastive Learningcited by 1
  21. 2024Emergent Communication Protocol Learning for Task Offloading in Industrial Internet of Thingscited by 1
  22. 2024Transferable Graphical MARL for Real-Time Estimation in Dynamic Wireless Networkscited by 1
  23. 2024A Multi-Agent Reinforcement Learning Testbed for Cognitive Radio Applicationscited by 1
  24. 2024Multi-Agent Deep Q-Network with Layer-based Communication Channel for Autonomous Internal Logistics Vehicle Scheduling in Smart Manufacturingcited by 1
  25. 2025Provable Performance Bounds for Digital Twin-driven Deep Reinforcement Learning in Wireless Networks: A Novel Digital-Twin Bisimulation Metriccited by 1
  26. 2026When Do Multi-Agent Systems Outperform? Analysing the Learning Efficiency of Agentic Systemscited by 1
  27. 2026Large Language Model (LLM)-enabled Reinforcement Learning for Wireless Network Optimizationcited by 1
  28. 2021A Glimpse of Physical Layer Decision Mechanisms: Facts, Challenges, and Remediescited by 0
  29. 2021Decentralized Multi-Agent Reinforcement Learning for Task Offloading Under Uncertaintycited by 0
  30. 2023Heterogeneous 360 Degree Videos in Metaverse: Differentiated Reinforcement Learning Approachescited by 0
  31. 2024A Survey on Applications of Reinforcement Learning in Spatial Resource Allocationcited by 0
  32. 2024GNN-Empowered Effective Partial Observation MARL Method for AoI Management in Multi-UAV Networkcited by 0
  33. 2024MA-CDMR: An Intelligent Cross-domain Multicast Routing Method based on Multiagent Deep Reinforcement Learning in Multi-domain SDWNcited by 0
  34. 2025Partially Decentralized Multi-Agent Q-Learning via Digital Cousins for Wireless Networkscited by 0
  35. 2025Finite-Time Global Optimality Convergence in Deep Neural Actor-Critic Methods for Decentralized Multi-Agent Reinforcement Learningcited by 0
  36. 2025Compositional Learning for Modular Multi-Agent Self-Organizing Networkscited by 0
  37. 2025Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Reviewcited by 0
  38. 2025Communication-Enhanced Tutoring for Efficient Decentralized Multi-Agent Reinforcement Learningcited by 0

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