  Batch Bayesian optimisation (BO) has been successfully applied to
hyperparameter tuning using parallel computing, but it is wasteful of
resources: workers that complete jobs ahead of others are left idle. We address
this problem by developing an approach, Penalising Locally for Asynchronous
Bayesian Optimisation on $k$ workers (PLAyBOOK), for asynchronous parallel BO.
We demonstrate empirically the efficacy of PLAyBOOK and its variants on
synthetic tasks and a real-world problem. We undertake a comparison between
synchronous and asynchronous BO, and show that asynchronous BO often
outperforms synchronous batch BO in both wall-clock time and number of function
evaluations.
