  Contextual policy search (CPS) is a class of multi-task reinforcement
learning algorithms that is particularly useful for robotic applications. A
recent state-of-the-art method is Contextual Covariance Matrix Adaptation
Evolution Strategies (C-CMA-ES). It is based on the standard black-box
optimization algorithm CMA-ES. There are two useful extensions of CMA-ES that
we will transfer to C-CMA-ES and evaluate empirically: ACM-ES, which uses a
comparison-based surrogate model, and aCMA-ES, which uses an active update of
the covariance matrix. We will show that improvements with these methods can be
impressive in terms of sample-efficiency, although this is not relevant any
more for the robotic domain.
