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）是一种强大的黑箱优化框架，通过系统性地平衡探索与利用，旨在高效学习未知系统的全局最优解。然而，将BO作为协调具有未知结构的多智能体系统中决策制定工具的研究尚不充分。本文研究了一个通过共享变量或约束耦合的多智能体网络上的黑箱优化问题，其中每个子问题被建模为仅使用局部数据的贝叶斯优化。所提出的多智能体BO（MABO）框架在传统BO采集函数中添加了一个惩罚项，以在不进行数据共享的情况下反映子系统间的耦合性。我们利用交替方向乘子法（ADMM）推导出该惩罚项的合适形式，从而使局部决策问题能够并行（且可能异步）求解。通过在智能交通系统中实现节能车队编队的案例，验证了所提MABO方法的有效性。