mlsanityData to 7 Oct 2026

Offline and Distributional Reinforcement Learning for Radio Resource Management

arXiv 2409.16764 ↗First posted 25 Sep 2024cs.LG cs.AI cs.MAcited by 3

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Abstract (arXiv)

Reinforcement learning (RL) has proved to have a promising role in future intelligent wireless networks. Online RL has been adopted for radio resource management (RRM), taking over traditional schemes. However, due to its reliance on online interaction with the environment, its role becomes limited in practical, real-world problems where online interaction is not feasible. In addition, traditional RL stands short in front of the uncertainties and risks in real-world stochastic environments. In this manner, we propose an offline and distributional RL scheme for the RRM problem, enabling offline training using a static dataset without any interaction with the environment and considering the sources of uncertainties using the distributions of the return. Simulation results demonstrate that the proposed scheme outperforms conventional resource management models. In addition, it is the only scheme that surpasses online RL with a 10 % gain over online RL.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 11 of its 20 references that are in the map. First posted on arXiv 25 Sep 2024.

Builds on

11 of 20 references.

References · 11

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  3. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,777
  4. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,179
  5. 2017Distributional Reinforcement Learning with Quantile Regressioncited by 464
  6. 2018Applications of Deep Reinforcement Learning in Communications and Networking: A Surveycited by 163
  7. 2020Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learningcited by 41
  8. 2022Learning Resilient Radio Resource Management Policies with Graph Neural Networkscited by 17
  9. 2023Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasetscited by 12
  10. 2024Conservative and Risk-Aware Offline Multi-Agent Reinforcement Learningcited by 10
  11. 2023Multi-UAV Path Learning for Age and Power Optimization in IoT with UAV Battery Rechargecited by 9

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Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025An Offline Multi-Agent Reinforcement Learning Framework for Radio Resource Managementcited by 5
  2. 2025Offline and Distributional Reinforcement Learning for Wireless Communicationscited by 3
  3. 2025Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learningcited by 1