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

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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.
Original languageEnglish
Pages123-139
Number of pages17
DOIs
Publication statusPublished - 2021
Event1st TAILOR workshop at ECAI 2020: International Workshop on the Foundations of Trustworthy AI Integrating Learning, Optimization and Reasoning - Santiago de Compostela, Spain
Duration: 4 Sept 20205 Sept 2020
https://liu.se/en/research/tailor/workshop

Workshop

Workshop1st TAILOR workshop at ECAI 2020
Country/TerritorySpain
CitySantiago de Compostela
Period4/09/205/09/20
Internet address

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