This paper proposes a framework called Watts for implementing, comparing, and recombining open-ended learning (OEL) algorithms. Motivated by modularity and algorithmic flexibility, Watts atomizes the components of OEL systems to promote the study of and direct comparisons between approaches. Examining implementations of three OEL algorithms, the paper introduces the modules of the framework. The hope is for Watts to enable benchmarking and to explore new types of OEL algorithms. The repo is available at \url{https://github.com/aadharna/watts}

翻译：本文件提出一个称为Watts的框架,用于实施、比较和重新组合开放式学习算法。在模块性和算法灵活性的驱动下,Watts将OEL系统的各个组成部分原子化,以促进对各种方法的研究和直接比较。文件审查了三种OEL算法的运用情况,介绍了框架的模块。希望Watts能够制定基准,并探索新的类型的OEL算法。可参见\url{https://github.com/aadharna/watts}