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We present GLASSES: Global optimisation with Look-Ahead through StochasticSimulation and Expected-loss Search. The majority of global optimisationapproaches in use are myopic, in only considering the impact of the nextfunction value; the non-myopic approaches that do exist are able to consideronly a handful of future evaluations. Our novel algorithm, GLASSES, permits theconsideration of dozens of evaluations into the future. This is done byapproximating the ideal look-ahead loss function, which is expensive toevaluate, by a cheaper alternative in which the future steps of the algorithmare simulated beforehand. An Expectation Propagation algorithm is used tocompute the expected value of the loss.We show that the far-horizon planningthus enabled leads to substantive performance gains in empirical tests.

GLASSES: Relieving The Myopia Of Bayesian Optimisation