Deep Reinforcement Learning for 6G AI-RAN: A Comprehensive Survey

[2608.14877] Deep Reinforcement Learning for 6G AI-RAN: A Comprehensive Survey Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Networking and Internet Architecture arXiv:2608.14877 (cs) [Submitted on 14 Aug 2026] Title: Deep Reinforcement Learning for 6G AI-RAN: A Comprehensive Survey Authors: Jie Lu , Peihao Yan , Qijun Wang , Ruxin Lin , Huacheng Zeng View a PDF of the paper titled Deep Reinforcement Learning for 6G AI-RAN: A Comprehensive Survey, by Jie Lu and 3 other authors View PDF HTML (experimental) Abstract: The evolution toward sixth-generation (6G) networks is transforming the radio access network (RAN) into a programmable and intelligent control platform that must continuously adapt to heterogeneous services, dynamic environments, and competing performance objectives. Open Radio Access Network (O-RAN) provides the open interfaces, disaggregated architecture, and multi-timescale control loops needed to support this transformation, while deep reinforcement learning (DRL) offers a natural framework for optimizing sequential decisions under uncertainty. However, existing surveys either address artificial intelligence (AI) and machine learning (ML) in O-RAN broadly or focus on isolated DRL use cases, leaving a gap in the systematic connection between DRL methodology, O-RAN architecture, and operational deployment. To the best of our knowledge, this article presents the first dedicated and comprehensive survey of DRL for Open AI-RAN. We review the foundations of model-free, model-based, offline, safe, multi-agent, federated, and transfer learning, and provide an O-RAN-aware framework for formulating RAN control problems through states, observations, actions, rewards, constraints, and temporal structure. We classify DRL applications across radio resource management, mobility management, interference control, traffic steering, energy efficiency, network slicing, integrated sensing and communication, security, and massive MIMO. We further examine multi-agent and federated coordination, foundation models and agentic AI, trustworthy DRL, sim-to-real transfer, continual adaptation, resource-efficient inference, and reinforcement learning operations. Finally, we review experimental platforms, benchmarks, standards, and industry activities, and identify research directions toward sample-efficient, safe, scalable, interoperable, and deployable DRL control for 6G Open AI-RAN. Subjects: Networking and Internet Architecture (cs.NI) ; Signal Processing (eess.SP) Cite as: arXiv:2608.14877 [cs.NI] (or arXiv:2608.14877v1 [cs.NI] for this version) https://doi.org/10.48550/arXiv.2608.14877 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jie Lu [ view email ] [v1] Fri, 14 Aug 2026 20:36:27 UTC (2,636 KB) Full-text links: Access Paper: View a PDF of the paper titled Deep Reinforcement Learning for 6G AI-RAN: A Comprehensive Survey, by Jie Lu and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.NI < prev | next > new | recent | 2026-08 Change to browse by: cs eess eess.SP References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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