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Bayesian Optimization (BO) is an effective method for optimizingexpensive-to-evaluate black-box functions with a wide range of applications forexample in robotics, system design and parameter optimization. However, scalingBO to problems with large input dimensions (>10) remains an open challenge. Inthis paper, we propose to leverage results from optimal control to scale BO tohigher dimensional control tasks and to reduce the need for manually selectingthe optimization domain. The contributions of this paper are twofold: 1) Weshow how we can make use of a learned dynamics model in combination with amodel-based controller to simplify the BO problem by focusing onto the mostrelevant regions of the optimization domain. 2) Based on (1) we present amethod to find an embedding in parameter space that reduces the effectivedimensionality of the optimization problem. To evaluate the effectiveness ofthe proposed approach, we present an experimental evaluation on real hardware,as well as simulated tasks including a 48-dimensional policy for a quadcopter.

Bayesian Optimization for Policy Search in High-Dimensional Systems via Automatic Domain Selection