论文标题
一项关于分布式在线优化和游戏的调查
A Survey on Distributed Online Optimization and Game
论文作者
论文摘要
在过去的十年中，分布式在线优化和游戏越来越多地研究了其在传感器网络，机器人技术（例如，分布式目标跟踪和形成控制），智能网格，深度学习等中的广泛应用。在这些问题中，有一个代理网络可能是合作的（即在线优化）或非合作（即在线游戏）通过本地信息交换。在动态甚至对手环境中，每个代理的局部成本函数通常是在时间变化的。在每个时间，必须根据手头的历史信息做出决定，而不了解成本功能的未来信息。对于这些问题，仍然缺乏全面的调查。本文旨在从问题设置，通信，计算，算法和性能的角度详细概述分布式在线优化和游戏。此外，还讨论了一些潜在的未来方向。
Distributed online optimization and game have been increasingly researched in the last decade, mostly motivated by its wide applications in sensor networks, robotics (e.g., distributed target tracking and formation control), smart grids, deep learning, and so forth. In these problems, there is a network of agents who may be cooperative (i.e., distributed online optimization) or noncooperative (i.e., online game) through local information exchanges. And the local cost function of each agent is often time-varying in dynamic and even adversarial environments. At each time, a decision must be made by each agent based on historical information at hand without knowing future information on cost functions. For these problems, a comprehensive survey is still lacking. This paper aims to provide a thorough overview of distributed online optimization and game from the perspective of problem settings, communication, computation, algorithms, and performances. In addition, some potential future directions are also discussed.
