In this paper, we study proximal type dynamics in the context of noncooperative multi-agent network games. These dynamics arise in different applications, since they describe distributed decision making in multi-agent networks, e.g., in opinion dynamics, distributed model fitting and network information fusion, where the goal of each agent is to seek an equilibrium using local information only. We analyse several conjugations of this class of games, providing convergence results, or designing equilibrium seeking algorithms when the original dynamics fail to converge. For the games subject only to local constraints we look into both synchronous/asynchronous dynamics and time-varying communication networks. For games subject in addition to coupling constraints, we design an equilibrium seeking algorithm converging to a special class of game equilibria. Finally, we validate the theoretical results via numerical simulations on opinion dynamics and distributed model fitting.

中文翻译：

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多智能体网络游戏中的异步和时变近端类型动力学

在本文中，我们研究了非合作多智能体网络游戏背景下的近端类型动力学。这些动态出现在不同的应用中，因为它们描述了多代理网络中的分布式决策，例如，在意见动态、分布式模型拟合和网络信息融合中，其中每个代理的目标是仅使用本地信息寻求平衡。我们分析了此类博弈的几种共轭，提供收敛结果，或在原始动力学未能收敛时设计平衡寻求算法。对于仅受局部约束的游戏，我们同时研究同步/异步动态和时变通信网络。对于除了耦合约束之外的游戏，我们设计了一种收敛于一类特殊博弈均衡的均衡寻求算法。最后，我们通过意见动态和分布式模型拟合的数值模拟验证了理论结果。