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

2026-01-01

IEEE wireless communications

AI Summary

This work proposes a novel offline multi-agent reinforcement learning (MARL) framework that integrates Conservative Q-Learning (CQL) with meta-learning to address the high cost, weak safety guarantees, and poor scalability of conventional online MARL in complex 6G networks. By uniquely combining offline MARL with meta-learning, the approach enhances both the safety of training and the ability to rapidly adapt to dynamic wireless environments. Experimental evaluations in wireless resource management and unmanned aerial vehicle (UAV) network scenarios demonstrate that the proposed method significantly outperforms existing solutions, highlighting the substantial potential and advantages of offline MARL for future 6G communication systems.

Technology Category

• Multiagent Systems: Multiagent Learning
• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• offline multi-agent reinforcement learning
• 6G communications
• network complexity
• scalability
• safety

Innovation

• offline multi-agent reinforcement learning
• conservative Q-learning
• meta-learning
• 6G communications
• radio resource management