Combining CMA-ES sampling with information gain query selection improved perceived behavioral adaptation and overall preference in a 14-person study, though ease-of-use gains over pure information gain were not significant.
IEEE transactions on Evolutionary Computation 1(1), 3–17 (1997)
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Improving User Experience in Preference-Based Optimization of Reward Functions for Assistive Robots
Combining CMA-ES sampling with information gain query selection improved perceived behavioral adaptation and overall preference in a 14-person study, though ease-of-use gains over pure information gain were not significant.