  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}
