QDax is an open-source library with a streamlined and modular API for Quality-Diversity (QD) optimization algorithms in Jax. The library serves as a versatile tool for optimization purposes, ranging from black-box optimization to continuous control. QDax offers implementations of popular QD, Neuroevolution, and Reinforcement Learning (RL) algorithms, supported by various examples. All the implementations can be just-in-time compiled with Jax, facilitating efficient execution across multiple accelerators, including GPUs and TPUs. These implementations effectively demonstrate the framework's flexibility and user-friendliness, easing experimentation for research purposes. Furthermore, the library is thoroughly documented and tested with 95\% coverage.

翻译：QDax是一个开源库，提供精简且模块化的API，用于Jax框架中的质量多样性（QD）优化算法。该库作为通用优化工具，应用范围涵盖黑盒优化到连续控制领域。QDax实现了主流QD算法、神经进化和强化学习（RL）算法，并附有丰富的示例支持。所有实现均可通过Jax进行即时编译，支持在包括GPU和TPU在内的多种加速器上高效执行。这些实现充分展现了该框架的灵活性与易用性，为研究实验提供了便利。此外，该库经过全面文档记录与测试，代码覆盖率达95%。