1 citations · 2 across the 4 of their papers we have counts for
7 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…
DEALS: Decentralized Expertise-Aware Load Serving for Multi-Agent LLM Systems
Jingjuan Huang, Wenbin Wang, Yanchuan Yin +2
Multi-agent systems (MAS) have recently emerged as an effective approach for coordinating large language model (LLM)-based agents to solve complex tasks through structured interact…
To Solve Bilevel Optimization with Nonconvex Lower Levels, We Need Second-Order Stationarity
Zhiyao Zhang, Menglu Yu, Alvaro Velasquez +2
Although bilevel optimization (BLO) has emerged as a powerful framework for addressing many complex and nested machine learning problems in recent years, most existing studies are…
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…
On the Hardness of Decentralized Multi-Agent Policy Evaluation under Byzantine Attacks
Hairi, Minghong Fang, Zifan Zhang +2
In this paper, we study a fully-decentralized multi-agent policy evaluation problem, which is an important sub-problem in cooperative multi-agent reinforcement learning, in the pre…
Byzantine-Resilient Decentralized Multi-Armed Bandits
Jingxuan Zhu, Alec Koppel, Alvaro Velasquez +1
In decentralized cooperative multi-armed bandits (MAB), each agent observes a distinct stream of rewards, and seeks to exchange information with others to select a sequence of arms…