Bayesian optimization (BO) is a powerful black-box optimization framework that looks to efficiently learn the global optimum of an unknown system by systematically trading-off between exploration and exploitation. However, the use of BO as a tool for coordinated decision-making in multi-agent systems with unknown structure has not been widely studied. This paper investigates a black-box optimization problem over a multi-agent network coupled via shared variables or constraints, where each subproblem is formulated as a BO that uses only its local data. The proposed multi-agent BO (MABO) framework adds a penalty term to traditional BO acquisition functions to account for coupling between the subsystems without data sharing. We derive a suitable form for this penalty term using alternating directions method of multipliers (ADMM), which enables the local decision-making problems to be solved in parallel (and potentially asynchronously). The effectiveness of the proposed MABO method is demonstrated on an intelligent transport system for fuel efficient vehicle platooning.

翻译：贝叶斯优化（BO）是一种强大的黑盒优化框架，通过有系统地权衡探索和利用来高效地学习未知系统的全局最优。但是，在多智能体系统中使用BO作为协调决策的工具来处理未知结构的问题尚未得到广泛研究。本文研究了一个黑盒优化问题，其中多个智能体通过共享变量或约束相互耦合，每个子问题都被制定为仅使用其本地数据的BO。所提出的多智能体BO（MABO）框架向传统的BO获取函数添加了一个惩罚项，以考虑子系统之间的耦合而不共享数据。我们使用交替方向乘法器（ADMM）推导出了合适的惩罚项形式，这使得可以并行解决本地决策问题（并可能异步）。提出的MABO方法的有效性在用于燃油高效车队编队的智能交通系统中得到了证明。