mlsanityData to 14 Oct 2026

Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networks

arXiv 1810.10408 ↗First posted 24 Oct 2018eess.SPcited by 56

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Abstract (arXiv)

Unmanned aerial vehicles (UAVs) are capable of serving as aerial base stations (BSs) for providing both cost-effective and on-demand wireless communications. This article investigates dynamic resource allocation of multiple UAVs enabled communication networks with the goal of maximizing long-term rewards. More particularly, each UAV communicates with a ground user by automatically selecting its communicating users, power levels and subchannels without any information exchange among UAVs. To model the uncertainty of environments, we formulate the long-term resource allocation problem as a stochastic game for maximizing the expected rewards, where each UAV becomes a learning agent and each resource allocation solution corresponds to an action taken by the UAVs. Afterwards, we develop a multi-agent reinforcement learning (MARL) framework that each agent discovers its best strategy according to its local observations using learning. More specifically, we propose an agent-independent method, for which all agents conduct a decision algorithm independently but share a common structure based on Q-learning. Finally, simulation results reveal that: 1) appropriate parameters for exploitation and exploration are capable of enhancing the performance of the proposed MARL based resource allocation algorithm; 2) the proposed MARL algorithm provides acceptable performance compared to the case with complete information exchanges among UAVs. By doing so, it strikes a good tradeoff between performance gains and information exchange overheads.

Key facts. Cited by 56 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Oct 2018.

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Citing papers per year: 2021: 10; 2022: 6; 2023: 10; 2024: 12; 2025: 14; 2026: 4.

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  1. 2021Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Surveycited by 34
  2. 2022Towards Comprehensive Testing on the Robustness of Cooperative Multi-agent Reinforcement Learningcited by 21
  3. 2021UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approachcited by 20
  4. 2024Group-Aware Coordination Graph for Multi-Agent Reinforcement Learningcited by 18
  5. 2025Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Surveycited by 14
  6. 2025UAVs Meet Agentic AI: A Multidomain Survey of Autonomous Aerial Intelligence and Agentic UAVscited by 13
  7. 2024Conservative and Risk-Aware Offline Multi-Agent Reinforcement Learningcited by 10
  8. 2024Toward Enhanced Reinforcement Learning-Based Resource Management via Digital Twin: Opportunities, Applications, and Challengescited by 10
  9. 2025Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learningcited by 9
  10. 2023Achieving Fairness in Multi-Agent Markov Decision Processes Using Reinforcement Learningcited by 8
  11. 2023Multi-Agent Reinforcement Learning Guided by Signal Temporal Logic Specificationscited by 7
  12. 2024Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement Learningcited by 7
38 more
  1. 2021Joint Deep Reinforcement Learning and Unfolding: Beam Selection and Precoding for mmWave Multiuser MIMO with Lens Arrayscited by 6
  2. 2023Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semanticscited by 5
  3. 2024CE-NAS: An End-to-End Carbon-Efficient Neural Architecture Search Frameworkcited by 5
  4. 2024Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learningcited by 5
  5. 2021Resource allocation in dynamic multiagent systemscited by 4
  6. 2025Optimizing Communication and Device Clustering for Clustered Federated Learning with Differential Privacycited by 4
  7. 2021Learning based signal detection for MIMO systems with unknown noise statisticscited by 3
  8. 2021Distributed Reinforcement Learning for Flexible and Efficient UAV Swarm Controlcited by 3
  9. 2021Energy-aware optimization of UAV base stations placement via decentralized multi-agent Q-learningcited by 3
  10. 2021Cooperative Multi-Agent Reinforcement Learning with Hypergraph Convolutioncited by 3
  11. 2022Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Controlcited by 3
  12. 2022UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithmscited by 3
  13. 2023Reliable and Efficient Data Collection in UAV-based IoT Networkscited by 3
  14. 2024Latency Minimization for UAV-Enabled Federated Learning: Trajectory Design and Resource Allocationcited by 3
  15. 2025Deep Reinforcement Learning based Autonomous Decision-Making for Cooperative UAVs: A Search and Rescue Real World Applicationcited by 3
  16. 2021Machine Learning Methods for Management UAV Flocks -- a Surveycited by 2
  17. 2022Distributed Control using Reinforcement Learning with Temporal-Logic-Based Reward Shapingcited by 2
  18. 2022Learning-Based Client Selection for Federated Learning Services Over Wireless Networks with Constrained Monetary Budgetscited by 2
  19. 2023A Hybrid Framework of Reinforcement Learning and Convex Optimization for UAV-Based Autonomous Metaverse Data Collectioncited by 2
  20. 2024Multi-Agent Reinforcement Learning for Offloading Cellular Communications with Cooperating UAVscited by 2
  21. 2024Quantum Multi-Agent Reinforcement Learning for Cooperative Mobile Access in Space-Air-Ground Integrated Networkscited by 2
  22. 2024Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approachcited by 2
  23. 2025HypRL: Reinforcement Learning of Control Policies for Hyperpropertiescited by 2
  24. 2025Multi-Agent Reinforcement Learning in Intelligent Transportation Systems: A Comprehensive Surveycited by 2
  25. 2023Muti-Agent Proximal Policy Optimization For Data Freshness in UAV-assisted Networkscited by 1
  26. 2023Collaborative Wideband Spectrum Sensing and Scheduling for Networked UAVs in UTM Systemscited by 1
  27. 2023UAV-assisted Semantic Communication with Hybrid Action Reinforcement Learningcited by 1
  28. 2024Federated Learning-based Collaborative Wideband Spectrum Sensing and Scheduling for UAVs in UTM Systemscited by 1
  29. 2024Distributed Stackelberg Strategies in State-based Potential Games for Autonomous Decentralized Learning Manufacturing Systemscited by 1
  30. 2025Intelligent Offloading in Vehicular Edge Computing: A Comprehensive Review of Deep Reinforcement Learning Approaches and Architecturescited by 1
  31. 2025Reinforcement Learning for Game-Theoretic Resource Allocation on Graphscited by 1
  32. 2025Accelerated Learning with Linear Temporal Logic using Differentiable Simulationcited by 1
  33. 2026Graphon Mean-Field Subsampling for Cooperative Heterogeneous Multi-Agent Reinforcement Learningcited by 1
  34. 2021Responsive Regulation of Dynamic UAV Communication Networks Based on Deep Reinforcement Learningcited by 0
  35. 2022Multi-Agent Reinforcement Learning for Long-Term Network Resource Allocation through Auction: a V2X Applicationcited by 0
  36. 2023UAV aided Metaverse over Wireless Communications: A Reinforcement Learning Approachcited by 0
  37. 2023Multiobjective Hydropower Reservoir Operation Optimization with Transformer-Based Deep Reinforcement Learningcited by 0
  38. 2025Achieving Optimal Tissue Repair Through MARL with Reward Shaping and Curriculum Learningcited by 0

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