4 papers
Foundation Model-Aided Multi-Agent Reinforcement Learning for Wireless Random Access Network Optimization
Myeung Suk Oh, Zhiyao Zhang, Alvaro Velasquez +2
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…
A Tale of Two Problems: Multi-Task Bilevel Learning Meets Equality Constrained Multi-Objective Optimization
Zhiyao Zhang, Myeung Suk Oh, Zhen Qin +3
In recent years, bilevel optimization (BLO) has attracted significant attention for its broad applications in machine learning. However, most existing works on BLO remain confined…
Consensus-based Decentralized Multi-agent Reinforcement Learning for Random Access Network Optimization
Myeung Suk Oh, Zhiyao Zhang, FNU Hairi +2
With wireless devices increasingly forming a unified smart network for seamless, user-friendly operations, random access (RA) medium access control (MAC) design is considered a key…
Finite-Time Global Optimality Convergence in Deep Neural Actor-Critic Methods for Decentralized Multi-Agent Reinforcement Learning
Zhiyao Zhang, Myeung Suk Oh, FNU Hairi +3
Actor-critic methods for decentralized multi-agent reinforcement learning (MARL) facilitate collaborative optimal decision making without centralized coordination, thus enabling a…