  In practice, the parameters of control policies are often tuned manually.
This is time-consuming and frustrating. Reinforcement learning is a promising
alternative that aims to automate this process, yet often requires too many
experiments to be practical. In this paper, we propose a solution to this
problem by exploiting prior knowledge from simulations, which are readily
available for most robotic platforms. Specifically, we extend Entropy Search, a
Bayesian optimization algorithm that maximizes information gain from each
experiment, to the case of multiple information sources. The result is a
principled way to automatically combine cheap, but inaccurate information from
simulations with expensive and accurate physical experiments in a
cost-effective manner. We apply the resulting method to a cart-pole system,
which confirms that the algorithm can find good control policies with fewer
experiments than standard Bayesian optimization on the physical system only.
