Quality-diversity in dissimilarity spaces

Published 14 Nov 2022 in cs.AI, cs.NE, and math.OC | (2211.12337v3)

Abstract: The theory of magnitude provides a mathematical framework for quantifying and maximizing diversity. We apply this framework to formulate quality-diversity algorithms in generic dissimilarity spaces. In particular, we instantiate and demonstrate a very general version of Go-Explore with promising performance.

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Authors (1)

Steve Huntsman

Citations (1)

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