mlsanityData to 7 Oct 2026

Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey

arXiv 2110.13484 ↗First posted 26 Oct 2021cs.AI cs.LG cs.MAcited by 34

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Abstract (arXiv)

Future Internet involves several emerging technologies such as 5G and beyond 5G networks, vehicular networks, unmanned aerial vehicle (UAV) networks, and Internet of Things (IoTs). Moreover, future Internet becomes heterogeneous and decentralized with a large number of involved network entities. Each entity may need to make its local decision to improve the network performance under dynamic and uncertain network environments. Standard learning algorithms such as single-agent Reinforcement Learning (RL) or Deep Reinforcement Learning (DRL) have been recently used to enable each network entity as an agent to learn an optimal decision-making policy adaptively through interacting with the unknown environments. However, such an algorithm fails to model the cooperations or competitions among network entities, and simply treats other entities as a part of the environment that may result in the non-stationarity issue. Multi-agent Reinforcement Learning (MARL) allows each network entity to learn its optimal policy by observing not only the environments, but also other entities' policies. As a result, MARL can significantly improve the learning efficiency of the network entities, and it has been recently used to solve various issues in the emerging networks. In this paper, we thus review the applications of MARL in the emerging networks. In particular, we provide a tutorial of MARL and a comprehensive survey of applications of MARL in next generation Internet. In particular, we first introduce single-agent RL and MARL. Then, we review a number of applications of MARL to solve emerging issues in future Internet. The issues consist of network access, transmit power control, computation offloading, content caching, packet routing, trajectory design for UAV-aided networks, and network security issues.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). Builds on 60 of its 193 references that are in the map. First posted on arXiv 26 Oct 2021.

Builds on

60 of 193 references.

References · 60

  1. 2017Attention Is All You Needcited by 59,932
  2. 2017Proximal Policy Optimization Algorithmscited by 14,441
  3. 2017Graph Attention Networkscited by 8,854
  4. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  5. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  6. 2015Continuous control with deep reinforcement learningcited by 3,279
  7. 2015Trust Region Policy Optimizationcited by 2,503
  8. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  9. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 1,916
  10. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
  11. 2016OpenAI Gymcited by 1,684
  12. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
38 more
  1. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
  2. 2015Prioritized Experience Replaycited by 1,070
  3. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 769
  4. 2017Counterfactual Multi-Agent Policy Gradientscited by 733
  5. 2018QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 697
  6. 2019Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithmscited by 555
  7. 2017Value-Decomposition Networks For Cooperative Multi-Agent Learningcited by 490
  8. 2017Deep Reinforcement Learning: An Overviewcited by 327
  9. 2018Actor-Attention-Critic for Multi-Agent Reinforcement Learningcited by 259
  10. 2013The Complexity of Decentralized Control of Markov Decision Processescited by 225
  11. 2018Mean Field Multi-Agent Reinforcement Learningcited by 202
  12. 2018An Introduction to Deep Reinforcement Learningcited by 201
  13. 2020Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 197
  14. 2018Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agentscited by 189
  15. 2018Applications of Deep Reinforcement Learning in Communications and Networking: A Surveycited by 163
  16. 2017Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximationcited by 119
  17. 2018Deep Learning in Mobile and Wireless Networking: A Surveycited by 117
  18. 2017A Survey of Learning in Multiagent Environments: Dealing with Non-Stationaritycited by 93
  19. 2018Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networkscited by 70
  20. 2017Deep Learning for IoT Big Data and Streaming Analytics: A Surveycited by 59
  21. 2018Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networkscited by 54
  22. 2020Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learningcited by 41
  23. 2019Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challengescited by 36
  24. 2018Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networkscited by 34
  25. 2019How You Act Tells a Lot: Privacy-Leakage Attack on Deep Reinforcement Learningcited by 29
  26. 2020Multi-Agent Deep Reinforcement Learning Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computingcited by 22
  27. 2020Cooperative Internet of UAVs: Distributed Trajectory Design by Multi-agent Deep Reinforcement Learningcited by 18
  28. 2013Distributed Policy Evaluation Under Multiple Behavior Strategiescited by 17
  29. 2017A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimizationcited by 14
  30. 2018Distributed Cooperative Spectrum Sharing in UAV Networks Using Multi-Agent Reinforcement Learningcited by 12
  31. 2018Multi-Tenant Cross-Slice Resource Orchestration: A Deep Reinforcement Learning Approachcited by 10
  32. 2018A Collaborative Multi-agent Reinforcement Learning Anti-jamming Algorithm in Wireless Networkscited by 10
  33. 2018Reinforcement Learning for Decentralized Trajectory Design in Cellular UAV Networks with Sense-and-Send Protocolcited by 8
  34. 2019Multi-Drone 3D Trajectory Planning and Scheduling in Drone Assisted Radio Access Networkscited by 6
  35. 2020Reward Design in Cooperative Multi-agent Reinforcement Learning for Packet Routingcited by 6
  36. 2018Finite-Sample Analysis For Decentralized Batch Multi-Agent Reinforcement Learning With Networked Agentscited by 5
  37. 2019Multi-Agent Deep Reinforcement Learning based Spectrum Allocation for D2D Underlay Communicationscited by 5
  38. 2020Deep Reinforcement Learning for Multi-Agent Power Control in Heterogeneous Networkscited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 5; 2024: 6; 2025: 13; 2026: 9.

34 papers.

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  1. 2025Multi-Agent Coordination across Diverse Applications: A Surveycited by 37
  2. 2025Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Surveycited by 14
  3. 2024Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectoriescited by 9
  4. 2024Applications of Generative AI (GAI) for Mobile and Wireless Networking: A Surveycited by 9
  5. 2024Towards Generalizability of Multi-Agent Reinforcement Learning in Graphs with Recurrent Message Passingcited by 8
  6. 2023A Revolution of Personalized Healthcare: Enabling Human Digital Twin with Mobile AIGCcited by 7
  7. 2025BLAST: A Stealthy Backdoor Leverage Attack against Cooperative Multi-Agent Deep Reinforcement Learning based Systemscited by 7
  8. 2025Deep Meta Coordination Graphs for Multi-agent Reinforcement Learningcited by 6
  9. 2023Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challengescited by 5
  10. 2024The Synergy Between Optimal Transport Theory and Multi-Agent Reinforcement Learningcited by 5
  11. 2025Multiplayer Federated Learning: Reaching Equilibrium with Less Communicationcited by 5
  12. 2025Federated Learning-based MARL for Strengthening Physical-Layer Security in B5G Networkscited by 5
22 more
  1. 2023Routing Recovery for UAV Networks with Deliberate Attacks: A Reinforcement Learning based Approachcited by 3
  2. 2025Joint Computation Offloading and Resource Allocation for Uncertain Maritime MEC via Cooperation of UAVs and Vesselscited by 3
  3. 2026Retaining Suboptimal Actions to Follow Shifting Optima in Multi-Agent Reinforcement Learningcited by 3
  4. 2022Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNetcited by 2
  5. 2025Exploring Incremental Unlearning: Techniques, Challenges, and Future Directionscited by 2
  6. 2026Action-Graph Policies: Learning Action Co-dependencies in Multi-Agent Reinforcement Learningcited by 2
  7. 2023Muti-Agent Proximal Policy Optimization For Data Freshness in UAV-assisted Networkscited by 1
  8. 2025Intelligent Offloading in Vehicular Edge Computing: A Comprehensive Review of Deep Reinforcement Learning Approaches and Architecturescited by 1
  9. 2025ReaCritic: Reasoning Transformer-based DRL Critic-model Scaling For Wireless Networkscited by 1
  10. 2023Intelligent Communication Planning for Constrained Environmental IoT Sensing with Reinforcement Learningcited by 0
  11. 2024A Network Simulation of OTC Markets with Multiple Agentscited by 0
  12. 2024FairStream: Fair Multimedia Streaming Benchmark for Reinforcement Learning Agentscited by 0
  13. 2025Partially Decentralized Multi-Agent Q-Learning via Digital Cousins for Wireless Networkscited by 0
  14. 2025Finite-Time Global Optimality Convergence in Deep Neural Actor-Critic Methods for Decentralized Multi-Agent Reinforcement Learningcited by 0
  15. 2025Communication-Enhanced Tutoring for Efficient Decentralized Multi-Agent Reinforcement Learningcited by 0
  16. 2026Offline Multi-Agent Reinforcement Learning for 6G Communications: Fundamentals, Applications and Future Directionscited by 0
  17. 2026Factored Value Functions for Graph-Based Multi-Agent Reinforcement Learningcited by 0
  18. 2026Blockchain-Enabled Routing for Zero-Trust Low-Altitude Intelligent Networkscited by 0
  19. 2026Energy-Efficient Federated Edge Learning For Small-Scale Datasets in Large IoT Networkscited by 0
  20. 2026Multi-Agent Systems: From Classical Paradigms to Large Foundation Model-Enabled Futurescited by 0
  21. 2026SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learningcited by 0
  22. 2026FedCritic-MIMO: Communication-Efficient Serverless Federated Critic Learning for Massive-MIMO Resource Control in Open and Disaggregated 6G RANscited by 0