Feb 2, 2022 · A common approach to speed up QD algorithms is to evaluate solutions in parallel, for instance by using physical simulators in robotics. Yet, ...
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 ...
Quality-Diversity (QD) algorithms are a well-known approach to generate large collections of diverse and high-quality policies. However, QD algorithms are ...
Feb 2, 2022 · ABSTRACT. Quality-Diversity (QD) algorithms are a well-known approach to generate large collections of diverse and high-quality policies.
In this paper, we present QDax, an implementation of MAP-Elites which leverages massive parallelism on accelerators to make QD algorithms more accessible. We ...
QDax is a tool to accelerate Quality-Diversity (QD) and neuro-evolution algorithms through hardware accelerators and massive parallelization.
Feb 1, 2022 · A common approach to speed up QD algorithms is to evaluate solutions in parallel, for instance by using physical simulators in robotics. Yet, ...
Feb 11, 2022 · Excited to share “Accelerated Quality-Diversity for Robotics through Massive Parallelism”. We introduce QDax which makes QD 100 times faster ...
Accelerated quality-diversity for robotics through massive parallelism. B Lim, M Allard, L Grillotti, A Cully. ICLR Workshop on Agent Learning in Open-Endedness ...
Model-based Quality-Diversity (QD) algorithms written in JAX. This library builds on the QDax library. Model-based QD algorithms can leverage the synergy ...