mlsanityData to 7 Oct 2026

Watts: Infrastructure for Open-Ended Learning

arXiv 2204.13250 ↗First posted 28 Apr 2022cs.AI cs.LG cs.NEcited by 2

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Abstract (arXiv)

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 urlhttps://github.com/aadharna/watts

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 17 of its 34 references that are in the map. First posted on arXiv 28 Apr 2022.

Builds on

17 of 34 references.

References · 17

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2017Proximal Policy Optimization Algorithmscited by 14,441
  3. 2016OpenAI Gymcited by 1,684
  4. 2017Evolution Strategies as a Scalable Alternative to Reinforcement Learningcited by 555
  5. 2017Ray: A Distributed Framework for Emerging AI Applicationscited by 516
  6. 2020Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Designcited by 260
  7. 2020Prioritized Level Replaycited by 190
  8. 2021Open-Ended Learning Leads to Generally Capable Agentscited by 177
  9. 2022Evolving Curricula with Regret-Based Environment Designcited by 166
  10. 2021Replay-Guided Adversarial Environment Designcited by 108
  11. 2020Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutionscited by 103
  12. 2020PCGRL: Procedural Content Generation via Reinforcement Learningcited by 90
5 more
  1. 2020Griddly: A platform for AI research in gamescited by 31
  2. 2021Pick Your Battles: Interaction Graphs as Population-Level Objectives for Strategic Diversitycited by 22
  3. 2020Learning to Generate Levels From Nothingcited by 17
  4. 2020Co-generation of game levels and game-playing agentscited by 14
  5. 2022It Takes Four to Tango: Multiagent Selfplay for Automatic Curriculum Generationcited by 14

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Cited by year

Citing papers per year: 2023: 1; 2024: 1.

2 papers.