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Differentiable Quality Diversity

NeurIPS 2021 · Optimization › Optimizers · approach: Randomized gradient estimation · 29 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use CMA-MEGA instead of derivative-free MAP-Elites (line)Linear Projection · LP (sphere)QD-score: 12.21 -> 75.29Helped
use CMA-MEGA instead of derivative-free MAP-Elites (line)Linear Projection · LP (Rastrigin)QD-score: 8.12 -> 62.54Helped
use CMA-MEGA instead of OG-MAP-Elites (line)Arm Repertoire · Arm RepertoireQD-score: 59.66 -> 74.18Helped
use Adam for CMA-MEGA updatesCMA-MEGA · LSIQD-score: 5.36 -> 21.82Helped
use Adam for CMA-MEGA updatesCMA-MEGA · LP (sphere)QD-score: 75.29 -> 75.30No clear effect
use Adam for CMA-MEGA updatesCMA-MEGA · Arm RepertoireQD-score: 74.18 -> 73.82No clear effect
add gradient normalization (ablation)OMG-MEGA · LP (sphere)QD-score: 56.96 ± 0.24 -> 71.58 ± 0.10Helped
add gradient normalization (ablation)OMG-MEGA · LP (Rastrigin)QD-score: 16.56 ± 0.11 -> 55.90 ± 0.21Helped
add gradient normalization (ablation)OMG-MEGA · Arm RepertoireQD-score: 0.85 ± 0.04 -> 44.12 ± 0.06Helped
adapt gradient coefficients with CMA-ES rather than using fixed-distribution coefficientsMEGA · LP (sphere)QD-score: 71.58 -> 75.29Helped
adapt gradient coefficients with CMA-ES rather than using fixed-distribution coefficientsMEGA · LP (Rastrigin)QD-score: 55.90 -> 62.54Helped

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