  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.
