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, distribute...
Experiments show that DEALS not only improves performance along multiple dimensions (e.g., answer accuracy and task throughput) in both homogeneous and heterogeneous agent pools, but also balances agent expertise and workload in a self-organized manner, enabling effective decentralized coordination.
Jing-Hang Huang, Wen-Bin Wang, Yan-Chuan Yin et al.· 0 citations
This work is the first to establish the finite-time convergence for achieving lower-level second-order stationary solutions in general LLNC-BLO and proposes the PROBE (Perturbed gradient algorithm for bilevel problem), which achieves a finite-time convergence rate of O(T^{-2/5}) where T denotes iterations.
Zhi-Yao Zhang, Meng-Lu Yu, Alvaro Velasquez et al.· 0 citations
This paper develops a differentiable framework for multi-objective MaxCut by combining an adjacency-based quadratic formulation with linear scalarization, thereby reducing the problem to a preference-conditioned single-objective signed-weight MaxCut problem.
Jing-Hang Huang, Cheng-Han Huang, Alvaro Velasquez et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
