  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.
