Quality diversity (QD) is a growing branch of stochastic optimization
research that studies the problem of generating an archive of solutions that
maximize a given objective function but are also diverse with respect to a set
of specified measure functions. However, even when these functions are
differentiable, QD algorithms treat them as "black boxes", ignoring gradient
information. We present the differentiable quality diversity (DQD) problem, a
special case of QD, where both the objective and measure functions are first
order differentiable. We then present MAP-Elites via a Gradient Arborescence
(MEGA), a DQD algorithm that leverages gradient information to efficiently
explore the joint range of the objective and measure functions. Results in two
QD benchmark domains and in searching the latent space of a StyleGAN show that
MEGA significantly outperforms state-of-the-art QD algorithms, highlighting
DQD's promise for efficient quality diversity optimization when gradient
information is available. Source code is available at
this https URL

提出了一种简化版的 Quality diversity 问题 - DQD，重点解决 Objective 和 Measure 函数是一阶可微分函数的问题，并提出了一种基于梯度信息的算法 MEGA, 成功地解决了优化过程中 “黑盒处理” 忽略 Gradient information 的问题。在两个 QD 基准领域和 StyleGAN 的潜在空间中进行实验，表明 MEGA 显著优于现有的 QD 算法，突出了 DQD 在梯度信息可用时高效的优势。