发表机构
University of Minnesota（明尼苏达大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多机器人部署的广义纳什均衡问题，提出无需乘子交换的完全分布式连续时间及离散时间算法，降低通信开销、提升隐私，经多机器人部署任务验证有效。
AI 中文摘要
近期机器学习研究日益关注非合作博弈中的均衡分析，而非仅聚焦于最优解。这类问题多涉及共享约束，可被建模为广义纳什均衡问题（GNEPs）。针对强单调博弈，现有方法通过交换拉格朗日乘子计算基于共识的变分广义纳什均衡（v-GNEs）。本文提出一种适用于共享线性等式约束的完全分布式连续时间算法，该算法无需乘子交换即可收敛并达到任意广义纳什均衡，降低了通信开销并提升了隐私性。本文还提供了离散时间方案，并在多机器人部署任务上验证了该方法。
英文摘要
Recent machine learning research has increasingly focused on equilibrium analysis in non-cooperative games rather than solely on optimal solutions. Many such problems involve shared constraints and can be formulated as Generalized Nash Equilibrium Problems (GNEPs). For strongly monotone games, existing methods compute consensus-based variational GNEs (v-GNEs) by exchanging Lagrange multipliers. We propose a fully distributed continuous-time algorithm for shared linear equality constraints that converges without multiplier exchange and reaches any GNE, reducing communication overhead and improving privacy. Discrete-time schemes are also provided, and the method is validated on a multi-robot placement task.
Comments6 pages, 3 figures. Published in the 2026 American Control Conference (ACC), pp. 3633--3638
Journal ref2026 American Control Conference (ACC), pp. 3633--3638, 2026
