  The performance of learning-based control techniques crucially depends on how
effectively the system is explored. While most exploration techniques aim to
achieve a globally accurate model, such approaches are generally unsuited for
systems with unbounded state spaces. Furthermore, a globally accurate model is
not required to achieve good performance in many common control applications,
e.g., local stabilization tasks. In this paper, we propose an active learning
strategy for Gaussian process state space models that aims to obtain an
accurate model on a bounded subset of the state-action space. Our approach aims
to maximize the mutual information of the exploration trajectories with respect
to a discretization of the region of interest. By employing model predictive
control, the proposed technique integrates information collected during
exploration and adaptively improves its exploration strategy. To enable
computational tractability, we decouple the choice of most informative data
points from the model predictive control optimization step. This yields two
optimization problems that can be solved in parallel. We apply the proposed
method to explore the state space of various dynamical systems and compare our
approach to a commonly used entropy-based exploration strategy. In all
experiments, our method yields a better model within the region of interest
than the entropy-based method.

Machine Learning;Systems and Control;Systems and Control;Machine Learning