  Bayesian optimization (BO) has demonstrated potential for optimizing control
performance in data-limited settings, especially for systems with unknown
dynamics or unmodeled performance objectives. The BO algorithm efficiently
trades-off exploration and exploitation by leveraging uncertainty estimates
using surrogate models. These surrogates are usually learned using data
collected from the target dynamical system to be optimized. Intuitively, the
convergence rate of BO is better for surrogate models that can accurately
predict the target system performance. In classical BO, initial surrogate
models are constructed using very limited data points, and therefore rarely
yield accurate predictions of system performance. In this paper, we propose the
use of meta-learning to generate an initial surrogate model based on data
collected from performance optimization tasks performed on a variety of systems
that are different to the target system. To this end, we employ deep kernel
networks (DKNs) which are simple to train and which comprise encoded Gaussian
process models that integrate seamlessly with classical BO. The effectiveness
of our proposed DKN-BO approach for speeding up control system performance
optimization is demonstrated using a well-studied nonlinear system with unknown
dynamics and an unmodeled performance function.
