论文标题
合作多代理系统的零订单反馈优化
Zeroth-Order Feedback Optimization for Cooperative Multi-Agent Systems
论文作者
论文摘要
我们研究了一类合作的多代理优化问题，每个代理都与本地行动向量和当地成本相关联，其目标是合作地找到将当地成本平均值最小化的联合行动概况。这些问题在许多应用中出现，例如分布式路由控制，风电场运行等。在许多此类问题中，梯度信息可能不容易获得，并且代理人只能观察他们的当地成本，因为他们的行动是确定新行动的反馈。在本文中，我们为我们考虑的问题类别提出了一个零级反馈优化方案，并为凸面和非convex设置提供明确的复杂性界限，并具有无声且嘈杂的局部成本观察。我们还简要讨论了代理之间对局部功能依赖性知识的影响。该算法的性能是通过分布式路由控制的数值示例来证明的。
We study a class of cooperative multi-agent optimization problems, where each agent is associated with a local action vector and a local cost, and the goal is to cooperatively find the joint action profile that minimizes the average of the local costs. Such problems arise in many applications, such as distributed routing control, wind farm operation, etc. In many of these problems, gradient information may not be readily available, and the agents may only observe their local costs incurred by their actions as a feedback to determine their new actions. In this paper, we propose a zeroth-order feedback optimization scheme for the class of problems we consider, and provide explicit complexity bounds for both the convex and nonconvex settings with noiseless and noisy local cost observations. We also discuss briefly on the impacts of knowledge of local function dependence between agents. The algorithm's performance is justified by a numerical example of distributed routing control.
