BayesOpt: A Library for Bayesian optimization with Robotics Applications
This is an incremental contribution that provides a practical tool for researchers and practitioners in robotics and related fields to streamline Bayesian optimization workflows.
The paper introduces BayesOpt, a library for Bayesian optimization that provides a fast and flexible toolbox for testing and combining different models and criteria, addressing the lack of clear standards in surrogate model selection.
The purpose of this paper is twofold. On one side, we present a general framework for Bayesian optimization and we compare it with some related fields in active learning and Bayesian numerical analysis. On the other hand, Bayesian optimization and related problems (bandits, sequential experimental design) are highly dependent on the surrogate model that is selected. However, there is no clear standard in the literature. Thus, we present a fast and flexible toolbox that allows to test and combine different models and criteria with little effort. It includes most of the state-of-the-art contributions, algorithms and models. Its speed also removes part of the stigma that Bayesian optimization methods are only good for "expensive functions". The software is free and it can be used in many operating systems and computer languages.