mlsanityData to 14 Oct 2026

Offline Multi-Agent Reinforcement Learning for 6G Communications: Fundamentals, Applications and Future Directions

arXiv 2601.00321 ↗First posted 1 Jan 2026cs.MAcited by 0

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

Abstract (arXiv)

The next-generation wireless technologies, including beyond 5G and 6G networks, are paving the way for transformative applications such as vehicle platooning, smart cities, and remote surgery. These innovations are driven by a vast array of interconnected wireless entities, including IoT devices, access points, UAVs, and CAVs, which increase network complexity and demand more advanced decision-making algorithms. Artificial intelligence (AI) and machine learning (ML), especially reinforcement learning (RL), are key enablers for such networks, providing solutions to high-dimensional and complex challenges. However, as networks expand to multi-agent environments, traditional online RL approaches face cost, safety, and scalability limitations. Offline multi-agent reinforcement learning (MARL) offers a promising solution by utilizing pre-collected data, reducing the need for real-time interaction. This article introduces a novel offline MARL algorithm based on conservative Q-learning (CQL), ensuring safe and efficient training. We extend this with meta-learning to address dynamic environments and validate the approach through use cases in radio resource management and UAV networks. Our work highlights offline MARL's advantages, limitations, and future directions in wireless applications.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Offline multi-agent RL, one of 13 directions in Offline RL and imitation learning from data. Builds on 10 of its 16 references that are in the map. First posted on arXiv 1 Jan 2026.

Builds on

10 of 16 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025An Offline Multi-Agent Reinforcement Learning Framework for Radio Resource Management22 Jan
  2. 2024Conservative and Risk-Aware Offline Multi-Agent Reinforcement Learning13 Feb

Other references · 8

  1. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  2. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,920
  3. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,809
  4. 2020Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorialcited by 60
  5. 2018Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networkscited by 56
  6. 20196G Wireless Communication Systems: Applications, Requirements, Technologies, Challenges, and Research Directionscited by 34
  7. 2021Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Surveycited by 34
  8. 2019Optimization for Reinforcement Learning: From Single Agent to Cooperative Agentscited by 20

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Close in wording, no citation either way

  1. 2025Offline and Distributional Reinforcement Learning for Wireless Communications
  2. 2025Multi-Agent Reinforcement Learning in Wireless Distributed Networks for 6G
  3. 2026Selecting Offline Reinforcement Learning Algorithms for Stochastic Network Control
  4. 2026Diffusion Offline Reinforcement Learning for Fair and Energy-Efficient UAV-Assisted Wireless Networks
  5. 2025Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning
  6. 2023Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets
  7. 2026Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking
  8. 2024Heterogeneous Multi-Agent Reinforcement Learning for Distributed Channel Access in WLANs