arXiv AI
Jun 4

Generalizable Multi-Task Learning for Wireless Networks Using Prompt Decision Transformers

arXiv:2606. 04328v1 Announce Type: cross Abstract: Future wireless networks demand rapid adaptation to highly heterogeneous environments and dynamic task configurations, necessitating a shift from conventional rule-based and optimization-driven radio resource management (RRM) toward artificial intelligence (AI)-driven RRM.

By Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci
arXiv Machine Learning
Aug 24

BIPPO: Budget-Aware Independent PPO for Energy-Efficient Federated Learning Services

BIPPO (Budget-aware Independent Proximal Policy Optimization) is a multi‑agent reinforcement learning framework designed for energy‑efficient client selection in federated learning (FL) over IoT systems. It addresses infrastructure constraints such as limited resources and device churn, which traditional FL and RL approaches overlook. Evaluated on two image‑classification tasks with non‑IID data, BIPPO improves mean accuracy over non‑RL methods, standard PPO, and IPPO while consuming only a negligible portion of the budget, even as client numbers grow.

By Anna Lackinger, Andrea Morichetta, Pantelis A. Frangoudis, Schahram Dustdar
arXiv AI
Oct 7

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.

By Myeung Suk Oh, Zhiyao Zhang, Alvaro Velasquez, Nathaniel D. Bastian, Jia Liu