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Bayesian最理想化中,我们常常寻求最大限度地减少现实世界物理系统中出现的黑箱目标功能。评估这种黑箱客观功能的成本的主要贡献者往往是为准备测量系统而需要付出的努力。我们考虑一种共同的假设方案,即随着连续评估之间的距离增加,准备成本会增加。在这一背景下,最理想化轨迹更可取,而标准近视(即,\一步至最佳)巴伊西亚优化方法所产生的跳跃路径是次最佳的。我们的算法(MONGOOSE)使用元偏差参数政策来产生平稳的优化轨迹,在以大移动成本优化功能时比现有方法取得绩效收益。