Workshop: Workshop on Agent Learning in Open-Endedness

Watts: Infrastructure for Open-Ended Learning

Aaron Dharna · Charlie Summers · Rohin Dasari · Julian Togelius · Amy Hoover


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 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. An anonymized repo is available at \url{}

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