无需交换乘子即可实现多机器人分布协同定位，通信更少更私密。
Fully Distributed GNE Algorithms for Multi-Robot Placement without Consensus on Multipliers
- 设计无需交换乘子的分布式连续时间算法
- 在共享线性等式约束下收敛至任意广义纳什均衡
- 适合对通信开销和隐私要求高的多机器人系统
近期机器学习研究愈发关注非合作博弈中的均衡分析，而非仅追求最优解。许多此类问题涉及共享约束，可建模为广义纳什均衡问题（GNEP）。对于强单调博弈，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-GNE）。本文提出一种完全分布式的连续时间算法，适用于共享线性等式约束，无需交换乘子即可收敛至任意GNE，显著降低通信开销并提升隐私性。同时提供离散时间方案，并在多机器人定位任务中验证了有效性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。