Foundation Model-Aided Multi-Agent Reinforcement Learning for Wireless Random Access Network Optimization

The paper introduces a foundation model–aided actor‑critic algorithm to boost multi‑agent reinforcement learning (MARL) for optimizing random access (RA) wireless networks. By embedding a foundation model into a consensus‑based decentralized MARL framework, the authors provide convergence analysis that matches conventional MARL under local reward exchanges and nonlinear value function approximations. Numerical experiments demonstrate that this FM‑based approach markedly accelerates MARL training for RA network optimization.

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• agents
• reinforcement-learning