  Contextual policy search allows adapting robotic movement primitives to
different situations. For instance, a locomotion primitive might be adapted to
different terrain inclinations or desired walking speeds. Such an adaptation is
often achievable by modifying a small number of hyperparameters. However,
learning, when performed on real robotic systems, is typically restricted to a
small number of trials. Bayesian optimization has recently been proposed as a
sample-efficient means for contextual policy search that is well suited under
these conditions. In this work, we extend entropy search, a variant of Bayesian
optimization, such that it can be used for active contextual policy search
where the agent selects those tasks during training in which it expects to
learn the most. Empirical results in simulation suggest that this allows
learning successful behavior with less trials.
