  Bayesian optimization is an efficient nonlinear optimization method where the
queries are carefully selected to gather information about the optimum
location. Thus, in the context of policy search, it has been called active
policy search. The main ingredients of Bayesian optimization for sample
efficiency are the probabilistic surrogate model and the optimal decision
heuristics. In this work, we exploit those to provide robustness to different
issues for policy search algorithms. We combine several methods and show how
their interaction works better than the sum of the parts. First, to deal with
input noise and provide a safe and repeatable policy we use an improved version
of unscented Bayesian optimization. Then, to deal with mismodeling errors and
improve exploration we use stochastic meta-policies for query selection and an
adaptive kernel. We compare the proposed algorithm with previous results in
several optimization benchmarks and robot tasks, such as pushing objects with a
robot arm, or path finding with a rover.
