mlsanityData to 7 Oct 2026

Advancing RAN Slicing with Offline Reinforcement Learning

arXiv 2312.10547 ↗First posted 16 Dec 2023cs.IT cs.LG cs.NIcited by 5

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

Abstract (arXiv)

Dynamic radio resource management (RRM) in wireless networks presents significant challenges, particularly in the context of Radio Access Network (RAN) slicing. This technology, crucial for catering to varying user requirements, often grapples with complex optimization scenarios. Existing Reinforcement Learning (RL) approaches, while achieving good performance in RAN slicing, typically rely on online algorithms or behavior cloning. These methods necessitate either continuous environmental interactions or access to high-quality datasets, hindering their practical deployment. Towards addressing these limitations, this paper introduces offline RL to solving the RAN slicing problem, marking a significant shift towards more feasible and adaptive RRM methods. We demonstrate how offline RL can effectively learn near-optimal policies from sub-optimal datasets, a notable advancement over existing practices. Our research highlights the inherent flexibility of offline RL, showcasing its ability to adjust policy criteria without the need for additional environmental interactions. Furthermore, we present empirical evidence of the efficacy of offline RL in adapting to various service-level requirements, illustrating its potential in diverse RAN slicing scenarios.

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 22 of its 44 references that are in the map. First posted on arXiv 16 Dec 2023.

Builds on

22 of 44 references.

Closest ancestors · 1

Same direction, newest first

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

Other references · 21

  1. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  2. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,777
  3. 2021Offline Reinforcement Learning with Implicit Q-Learningcited by 1,245
  4. 2018Off-Policy Deep Reinforcement Learning without Explorationcited by 1,227
  5. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,179
  6. 2021Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemblecited by 297
  7. 2021Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimismcited by 275
  8. 2021Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learningcited by 157
  9. 2022When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?cited by 86
  10. 2022Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Gamecited by 53
  11. 2020Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learningcited by 41
  12. 2018Towards Optimal Power Control via Ensembling Deep Neural Networkscited by 38
9 more
  1. 2021ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platformscited by 33
  2. 2019Experienced Deep Reinforcement Learning with Generative Adversarial Networks (GANs) for Model-Free Ultra Reliable Low Latency Communicationcited by 21
  3. 2019A Deep Q-Learning Method for Downlink Power Allocation in Multi-Cell Networkscited by 7
  4. 2021RAN Resource Slicing in 5G Using Multi-Agent Correlated Q-Learningcited by 7
  5. 2021OnSlicing: Online End-to-End Network Slicing with Reinforcement Learningcited by 7
  6. 2022Inter-Cell Slicing Resource Partitioning via Coordinated Multi-Agent Deep Reinforcement Learningcited by 5
  7. 2023Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraintscited by 4
  8. 2023Provably Efficient Offline Reinforcement Learning with Perturbed Data Sourcescited by 3
  9. 2017Small Cell Transmit Power Assignment Based on Correlated Bandit Learningcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 2.

5 papers.

  1. 2025Average Reward Reinforcement Learning for Wireless Radio Resource Managementcited by 3
  2. 2024Learning to Slice Wi-Fi Networks: A State-Augmented Primal-Dual Approachcited by 2
  3. 2025Decision Feedback In-Context Learning for Wireless Symbol Detectioncited by 0
  4. 2026Selecting Offline Reinforcement Learning Algorithms for Stochastic Network Controlcited 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. 2025An Offline Multi-Agent Reinforcement Learning Framework for Radio Resource Management
  2. 2025ORAN-GUIDE: RAG-Driven Prompt Learning for LLM-Augmented Reinforcement Learning in O-RAN Network Slicing
  3. 2024Offline Reinforcement Learning and Sequence Modeling for Downlink Link Adaptation
  4. 2025Generative Resource Allocation for 6G O-RAN with Diffusion Policies
  5. 2026Adaptive RAN Slicing Control via Reward-Free Self-Finetuning Agents
  6. 2024NetworkGym: Reinforcement Learning Environments for Multi-Access Traffic Management in Network Simulation
  7. 2025Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models
  8. 2026Generalizable Multi-Task Learning for Wireless Networks Using Prompt Decision Transformers