  Quality-Diversity (QD) optimization algorithms are a well-known approach to
generate large collections of diverse and high-quality solutions. However,
derived from evolutionary computation, QD algorithms are population-based
methods which are known to be data-inefficient and requires large amounts of
computational resources. This makes QD algorithms slow when used in
applications where solution evaluations are computationally costly. A common
approach to speed up QD algorithms is to evaluate solutions in parallel, for
instance by using physical simulators in robotics. Yet, this approach is
limited to several dozen of parallel evaluations as most physics simulators can
only be parallelized more with a greater number of CPUs. With recent advances
in simulators that run on accelerators, thousands of evaluations can now be
performed in parallel on single GPU/TPU. In this paper, we present QDax, an
accelerated implementation of MAP-Elites which leverages massive parallelism on
accelerators to make QD algorithms more accessible. We show that QD algorithms
are ideal candidates to take advantage of progress in hardware acceleration. We
demonstrate that QD algorithms can scale with massive parallelism to be run at
interactive timescales without any significant effect on the performance.
Results across standard optimization functions and four neuroevolution
benchmark environments shows that experiment runtimes are reduced by two
factors of magnitudes, turning days of computation into minutes. More
surprising, we observe that reducing the number of generations by two orders of
magnitude, and thus having significantly shorter lineage does not impact the
performance of QD algorithms. These results show that QD can now benefit from
hardware acceleration, which contributed significantly to the bloom of deep
learning.
