Safe Learning and Optimization Techniques: Towards a Survey of the State of the Art
It provides a survey for researchers in machine learning and optimization, but it is incremental as it updates and expands upon previous reviews.
This paper reviews safe learning and optimization algorithms from multiple domains, including reinforcement learning and Gaussian processes, to address problems that avoid evaluating non-safe input points, but it does not present new results or concrete numbers.
Safe learning and optimization deals with learning and optimization problems that avoid, as much as possible, the evaluation of non-safe input points, which are solutions, policies, or strategies that cause an irrecoverable loss (e.g., breakage of a machine or equipment, or life threat). Although a comprehensive survey of safe reinforcement learning algorithms was published in 2015, a number of new algorithms have been proposed thereafter, and related works in active learning and in optimization were not considered. This paper reviews those algorithms from a number of domains including reinforcement learning, Gaussian process regression and classification, evolutionary algorithms, and active learning. We provide the fundamental concepts on which the reviewed algorithms are based and a characterization of the individual algorithms. We conclude by explaining how the algorithms are connected and suggestions for future research.