arXiv:2606. 00759v1 Announce Type: new Abstract: Recent advances in artificial intelligence have expanded the focus from classical optimization to include equilibrium analysis in noncooperative games.
By Shao-An Yin
The paper presents a distributed primal‑dual algorithm for quadratic Generalized Nash Equilibrium Problems (GNEPs) that eliminates the requirement for multiplier consensus. By removing shared multipliers, the method reduces communication overhead and enhances privacy, allowing different initializations to converge to distinct GNEs, including non‑variational equilibria. Convergence is proven under sufficient conditions using an input‑to‑state stability (ISS) framework.
By Shao-An Yin
arXiv:2607. 25835v1 Announce Type: new Abstract: Distributed constraint optimization problems (DCOPs) provide a popular framework for distributed decision making under limited communication, but many real-world instances are too large to solve monolithically.
By Itai Zilberstein, Pranav Rajbhandari, Steve Chien, Tuomas Sandholm
arXiv:2603. 03741v2 Announce Type: replace-cross Abstract: To improve generalization and resilience in human-robot collaboration (HRC), robots must contend with diverse combinations of human behaviors and contexts, motivating multi-agent reinforcement learning (MARL).
By Hao Zhang, Yaru Niu, Yikai Wang, Ding Zhao, H. Eric Tseng
arXiv:2608.29562v1 Announce Type: new
Abstract: Ensuring the safe operation of multi-agent systems (MASs) under uncertain environments is crucial for cooperative robotic, where external disturbances...
By Xiaobing Dai, Zewen Yang, Wei Ren, Sandra Hirche
arXiv:2606. 30072v1 Announce Type: new Abstract: Cooperative tasks in Multi-Agent Reinforcement Learning (MARL) require agents to collectively maximize a shared return.
By Daiki E. Matsunaga, Junho Na, Tri Wahyu Guntara, Scott Sanner, Pascal Poupart, Jongmin Lee, Kee-Eung Kim