Foundation models speed up multi-agent learning for wireless networks
Foundation Model-Aided Multi-Agent Reinforcement Learning for Wireless Random Access Network Optimization
Machine LearningArtificial Intelligence
Summary
Random access is a basic method that helps many devices share a wireless network. The authors found that using a foundation model—a kind of general AI—can help multiple agents learn how to manage this sharing faster and more efficiently. Their approach works well even when agents only share local information, and it converges as quickly as older methods. This means wireless networks can adapt and optimize without long training delays.
What this means in practice
- •For wireless network operators: Optimize how devices self-manage their data transmissions in unpredictable traffic situations faster and with less training overhead.
- •For telecommunications equipment developers: Build smarter wireless access points that use foundation model-aided learning to improve network efficiency across various settings.
Authors
Myeung Suk Oh, Zhiyao Zhang, Alvaro Velasquez, Nathaniel D. Bastian, Jia Liu
Abstract
Random access (RA) is one of the most foundational medium access control (MAC) layer scheduling schemes for handling unpredictable data traffic from multiple terminals. While multi-agent reinforcement learning (MARL) has been explored to optimize RA-based wireless networks, its reliance on experience-driven, distributed policy learning incurs significant training overhead for each optimization task, limiting its feasibility in real-world applications. In this work, we propose to leverage a foundation model (FM) to improve MARL efficiency across diverse RA network optimization tasks. Specifically, we design an FM-aided actor-critic algorithm within a consensus-based decentralized MARL architecture and provide its convergence analysis under local reward exchanges and nonlinear value function approximations to show that our algorithm achieves the same convergence order as the conventional MARL with critic model exchanges and linear approximations. Our numerical results show that our FM-based approach significantly enhances MARL speed for RA network optimization.