AI RESEARCH
Discount Model Search for Quality Diversity Optimization in High-Dimensional Measure Spaces
arXiv CS.LG
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ArXi:2601.01082v5 Announce Type: replace Quality diversity (QD) optimization searches for a collection of solutions that optimize an objective while attaining diverse outputs of a user-specified, vector-valued measure function. Contemporary QD algorithms are typically limited to low-dimensional measures because high-dimensional measures are prone to distortion, where many solutions found by the QD algorithm map to similar measures. For example, the state-of-the-art CMA-MAE algorithm guides measure space exploration with a histogram in measure space that records so-called discount values.