Watts: Infrastructure for Open-Ended Learning

Published in ALOE Workshop 2022 @ ICLR, 2022

Recommended citation: see below http://aadharna.github.io/files/watts.pdf

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}

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Citation and link inside post.

  doi = {10.48550/ARXIV.2204.13250},
  url = {https://arxiv.org/abs/2204.13250},
  author = {Dharna, Aaron and Summers, Charlie and Dasari, Rohin and Togelius, Julian and Hoover, Amy K.},
  keywords = {Artificial Intelligence (cs.AI), Machine Learning (cs.LG), Neural and Evolutionary Computing (cs.NE), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Watts: Infrastructure for Open-Ended Learning},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Zero v1.0 Universal}