mlsanityData to 14 Oct 2026

Multi-Agent Conditional Diffusion Model with Mean Field Communication as Wireless Resource Allocation Planner

arXiv 2510.22969 ↗First posted 27 Oct 2025cs.AI cs.MAcited by 1

On the map: Diffusion models → Diffusion for compression and wireless

Abstract (arXiv)

In wireless communication systems, efficient and adaptive resource allocation plays a crucial role in enhancing overall Quality of Service (QoS). Compared to the conventional Model-Free Reinforcement Learning (MFRL) scheme, Model-Based RL (MBRL) first learns a generative world model for subsequent planning. The reuse of historical experience in MBRL promises more stable training behavior, yet its deployment in large-scale wireless networks remains challenging due to high-dimensional stochastic dynamics, strong inter-agent cooperation, and communication constraints. To overcome these challenges, we propose the Multi-Agent Conditional Diffusion Model Planner (MA-CDMP) for decentralized communication resource management. Built upon the Distributed Training with Decentralized Execution (DTDE) paradigm, MA-CDMP models each communication node as an autonomous agent and employs Diffusion Models (DMs) to capture and predict environment dynamics. Meanwhile, an inverse dynamics model guides action generation, thereby enhancing sample efficiency and policy scalability. Moreover, to approximate large-scale agent interactions, a Mean-Field (MF) mechanism is introduced as an assistance to the classifier in DMs. This design mitigates inter-agent non-stationarity and enhances cooperation with minimal communication overhead in distributed settings. We further theoretically establish an upper bound on the distributional approximation error introduced by the MF-based diffusion generation, guaranteeing convergence stability and reliable modeling of multi-agent stochastic dynamics. Extensive experiments demonstrate that MA-CDMP consistently outperforms existing MARL baselines in terms of average reward and QoS metrics, showcasing its scalability and practicality for real-world wireless network optimization.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Diffusion for compression and wireless, one of 14 directions in Diffusion models. Builds on 40 of its 77 references that are in the map. First posted on arXiv 27 Oct 2025.

Builds on

40 of 77 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Diffusion Model Based Resource Allocation Strategy in Ultra-Reliable Wireless Networked Control Systems22 Jul
  2. 2023Deep Generative Model and Its Applications in Efficient Wireless Network Management: A Tutorial and Case Study30 Mar

Other references · 38

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  4. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  5. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  6. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  7. 2022Classifier-Free Diffusion Guidancecited by 5,847
  8. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  9. 2015Continuous control with deep reinforcement learningcited by 3,407
  10. 2022DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Stepscited by 1,979
  11. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,809
  12. 2021Decision Transformer: Reinforcement Learning via Sequence Modelingcited by 1,546
26 more
  1. 2022Planning with Diffusion for Flexible Behavior Synthesiscited by 1,061
  2. 2021A Minimalist Approach to Offline Reinforcement Learningcited by 899
  3. 2018Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Modelscited by 774
  4. 2019When to Trust Your Model: Model-Based Policy Optimizationcited by 773
  5. 2022Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learningcited by 562
  6. 2022Is Conditional Generative Modeling all you need for Decision-Making?cited by 516
  7. 2020Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 485
  8. 2022Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptionscited by 445
  9. 2022Improved Analysis of Score-based Generative Modeling: User-Friendly Bounds under Minimal Smoothness Assumptionscited by 219
  10. 2018Mean Field Multi-Agent Reinforcement Learningcited by 207
  11. 2022Convergence of score-based generative modeling for general data distributionscited by 181
  12. 2023MADiff: Offline Multi-agent Learning with Diffusion Modelscited by 75
  13. 2021Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectificationcited by 61
  14. 2021Learning Markov State Abstractions for Deep Reinforcement Learningcited by 44
  15. 2022Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregationcited by 36
  16. 2020The Emergence of Adversarial Communication in Multi-Agent Reinforcement Learningcited by 28
  17. 2021Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysiscited by 26
  18. 2019Scalable Reinforcement Learning for Multi-Agent Networked Systemscited by 24
  19. 2024Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directionscited by 20
  20. 2019Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learningcited by 19
  21. 2024DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approachcited by 17
  22. 2023Improving Generalization and Data Efficiency with Diffusion in Offline Multi-agent RLcited by 15
  23. 2023Reward-Consistent Dynamics Models are Strongly Generalizable for Offline Reinforcement Learningcited by 12
  24. 2021Dynamic Multichannel Access via Multi-agent Reinforcement Learning: Throughput and Fairness Guaranteescited by 4
  25. 2022Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNetcited by 2
  26. 2025Conditional Diffusion Model with OOD Mitigation as High-Dimensional Offline Resource Allocation Planner in Clustered Ad Hoc Networkscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026From Denoising to Decision Making: A Survey on Diffusion Model-Enabled Deep Reinforcement Learning for Wireless Networkscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models
  2. 2025Diffusion Models for Future Networks and Communications: A Comprehensive Survey
  3. 2025Partially Decentralized Multi-Agent Q-Learning via Digital Cousins for Wireless Networks
  4. 2024A Multi-Agent Multi-Environment Mixed Q-Learning for Partially Decentralized Wireless Network Optimization
  5. 2026Foundation Model-Aided Multi-Agent Reinforcement Learning for Wireless Random Access Network Optimization
  6. 2025An Offline Multi-Agent Reinforcement Learning Framework for Radio Resource Management
  7. 2026Hybrid Offline-Online Multi-Agent Decision Transformers for Wireless Resource Management
  8. 2025Generative Resource Allocation for 6G O-RAN with Diffusion Policies