mlsanityData to 7 Oct 2026

An Offline Multi-Agent Reinforcement Learning Framework for Radio Resource Management

arXiv 2501.12991 ↗First posted 22 Jan 2025cs.MA cs.LGcited by 5

On the map: Offline RL and imitation learning from data → Offline multi-agent RL

Abstract (arXiv)

Offline multi-agent reinforcement learning (MARL) addresses key limitations of online MARL, such as safety concerns, expensive data collection, extended training intervals, and high signaling overhead caused by online interactions with the environment. In this work, we propose an offline MARL algorithm for radio resource management (RRM), focusing on optimizing scheduling policies for multiple access points (APs) to jointly maximize the sum and tail rates of user equipment (UEs). We evaluate three training paradigms: centralized, independent, and centralized training with decentralized execution (CTDE). Our simulation results demonstrate that the proposed offline MARL framework outperforms conventional baseline approaches, achieving over a 15% improvement in a weighted combination of sum and tail rates. Additionally, the CTDE framework strikes an effective balance, reducing the computational complexity of centralized methods while addressing the inefficiencies of independent training. These results underscore the potential of offline MARL to deliver scalable, robust, and efficient solutions for resource management in dynamic wireless networks.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in Offline multi-agent RL, one of 13 directions in Offline RL and imitation learning from data. Builds on 23 of its 44 references that are in the map. First posted on arXiv 22 Jan 2025.

Builds on

23 of 44 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets19 Nov

Other references · 22

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  2. 2015Trust Region Policy Optimizationcited by 2,503
  3. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  4. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,777
  5. 2021Offline Reinforcement Learning with Implicit Q-Learningcited by 1,245
  6. 2018Off-Policy Deep Reinforcement Learning without Explorationcited by 1,227
  7. 2017Value-Decomposition Networks For Cooperative Multi-Agent Learningcited by 490
  8. 2015Multiagent Cooperation and Competition with Deep Reinforcement Learningcited by 180
  9. 2018Applications of Deep Reinforcement Learning in Communications and Networking: A Surveycited by 163
  10. 2019A Review of Cooperative Multi-Agent Deep Reinforcement Learningcited by 147
  11. 2018Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networkscited by 70
  12. 2020Value-Decomposition Multi-Agent Actor-Criticscited by 47
10 more
  1. 2020Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learningcited by 41
  2. 2023Machine Learning for Large-Scale Optimization in 6G Wireless Networkscited by 18
  3. 2022Learning Resilient Radio Resource Management Policies with Graph Neural Networkscited by 17
  4. 2023Multi-UAV Path Learning for Age and Power Optimization in IoT with UAV Battery Rechargecited by 9
  5. 2022Multi-agent Reinforcement Learning for Dynamic Resource Management in 6G in-X Subnetworkscited by 8
  6. 2022Traffic Prediction and Fast Uplink for Hidden Markov IoT Modelscited by 8
  7. 2023Age Minimization in Massive IoT via UAV Swarm: A Multi-agent Reinforcement Learning Approachcited by 5
  8. 2024Traffic Learning and Proactive UAV Trajectory Planning for Data Uplink in Markovian IoT Modelscited by 3
  9. 2024Offline and Distributional Reinforcement Learning for Radio Resource Managementcited by 3
  10. 2014Game-theoretic Resource Allocation Methods for Device-to-Device (D2D) Communicationcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learningcited by 1
  2. 2025Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networkscited by 0
  3. 2026Offline Multi-Agent Reinforcement Learning for 6G Communications: Fundamentals, Applications and Future Directionscited by 0
  4. 2026Heterogeneous Multi-Agent Reinforcement Learning for Radio Resource Management under Coupled Finite-Horizon Constraintscited by 0
  5. 2026Hybrid Offline-Online Multi-Agent Decision Transformers for Wireless Resource Managementcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Advancing RAN Slicing with Offline Reinforcement Learning
  2. 2026Foundation Model-Aided Multi-Agent Reinforcement Learning for Wireless Random Access Network Optimization
  3. 2026Selecting Offline Reinforcement Learning Algorithms for Stochastic Network Control
  4. 2024Offline Reinforcement Learning and Sequence Modeling for Downlink Link Adaptation
  5. 2026Generalizable Multi-Task Learning for Wireless Networks Using Prompt Decision Transformers
  6. 2024Conservative and Risk-Aware Offline Multi-Agent Reinforcement Learning
  7. 2026Large Language Model (LLM)-enabled Reinforcement Learning for Wireless Network Optimization
  8. 2025AURA: Adaptive Unified Reasoning and Automation with LLM-Guided MARL for NextG Cellular Networks