Problem
Research questions and friction points this paper is trying to address.
Random Access
Multi-Agent Reinforcement Learning
Training Overhead
Wireless Network Optimization
Foundation Model-Aided Multi-Agent Reinforcement Learning for Wireless Random Access Network Optimization
🤖 AI Summary
This study addresses the prohibitive training overhead and deployment challenges of multi-agent reinforcement learning (MARL) in optimizing wireless random access networks. To overcome these bottlenecks, this work proposes a foundation model (FM)-assisted decentralized consensus MARL framework and designs an FM-driven Actor-Critic algorithm. Furthermore, it establishes that the proposed method achieves convergence rates comparable to conventional linear approaches under nonlinear value function approximation. Experimental results demonstrate that this approach significantly enhances learning efficiency and accelerates convergence across diverse random access tasks while substantially reducing training costs. Ultimately, this research provides both theoretical guarantees and a practical methodology for leveraging foundation models in the distributed optimization of wireless networks.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Foundation Model
Multi-Agent Reinforcement Learning
Random Access
Actor-Critic
Decentralized MARL
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