  In this paper, we learn dynamics models for parametrized families of
dynamical systems with varying properties. The dynamics models are formulated
as stochastic processes conditioned on a latent context variable which is
inferred from observed transitions of the respective system. The probabilistic
formulation allows us to compute an action sequence which, for a limited number
of environment interactions, optimally explores the given system within the
parametrized family. This is achieved by steering the system through
transitions being most informative for the context variable. We demonstrate the
effectiveness of our method for exploration on a non-linear toy-problem and two
well-known reinforcement learning environments.
