Safe Learning and Optimization Techniques: Towards a Survey of the State of the Art

• URL: http://arxiv.org/abs/2101.09505v2
• Date: Thu, 18 Feb 2021 13:38:59 GMT
• Title: Safe Learning and Optimization Techniques: Towards a Survey of the State of the Art
• Authors: Youngmin Kim, Richard Allmendinger and Manuel L\'opez-Ib\'a\~nez
• Abstract summary: Safe learning and optimization deals with learning and optimization problems that avoid, as much as possible, the evaluation of non-safe input points. A comprehensive survey of safe reinforcement learning algorithms was published in 2015, but 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.
• Score: 3.6954802719347413
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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