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.

QDax 是一个开源库，具有简化且模块化的 API，用于 Quality-Diversity (QD) 优化算法在 Jax 中。该库可用于各种优化目的，从黑盒优化到连续控制。QDax 提供了流行的 QD、神经进化和增强学习算法的实现，支持各种示例。所有这些实现都可以使用 Jax 进行即时编译，以便在多个加速器（包括 GPU 和 TPU）上进行高效执行。这些实现有效地展示了该框架的灵活性和用户友好性，为研究目的简化了实验。此外，该库有详细的文档，并通过测试覆盖率达到 95%。