In Bayesian optimisation, we often seek to minimise the black-box objective functions that arise in real-world physical systems. A primary contributor to the cost of evaluating such black-box objective functions is often the effort required to prepare the system for measurement. We consider a common scenario where preparation costs grow as the distance between successive evaluations increases. In this setting, smooth optimisation trajectories are preferred and the jumpy paths produced by the standard myopic (i.e.\ one-step-optimal) Bayesian optimisation methods are sub-optimal. Our algorithm, MONGOOSE, uses a meta-learnt parametric policy to generate smooth optimisation trajectories, achieving performance gains over existing methods when optimising functions with large movement costs.

翻译：在贝叶斯优化中，我们通常需要最小化真实物理系统中出现的黑箱目标函数。评估此类黑箱目标函数的主要成本之一往往来自为测量进行系统准备工作所需的投入。本文考虑一种常见场景：当连续评估点间距离增大时，准备工作成本随之增加。在此设定下，平滑的优化轨迹更受青睐，而标准短视（即单步最优）贝叶斯优化方法产生的跳跃路径并非最优。我们提出的算法MONGOOSE采用元学习参数化策略生成平滑优化轨迹，在优化具有高移动成本的目标函数时，相较于现有方法取得了性能提升。