Localized active learning of Gaussian process state space models
This work addresses the challenge of model exploration for control tasks in systems with unbounded state spaces, offering a domain-specific improvement for localized stabilization applications.
The paper tackled the problem of efficiently exploring unbounded state spaces for learning-based control by proposing an active learning strategy for Gaussian process state space models that focuses on achieving accurate models within a bounded region of interest, resulting in better model performance compared to an entropy-based method in experiments.
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.