/awesome-open-ended

Awesome Open-ended AI

Awesome Open-Ended AI Awesome

A curated list of open-ended learning AI resources. The aim of open-ended algorithms is to keep on inventing new and ever-more complex tasks and solving them continually, even endlessly. From the invention of the wheel, to farming, vaccines, computers, and even rock and roll. These so-far uniquely human advancements and discoveries are the hallmark of civilization. What does AI need to possess to discover such new paradigms, as only humans have until now? Let's take a look at our progress on this frontier. How close are we to AGI?

Table of Contents

Contributing

When submitting a pull request, please put the new paper at the correct chronological position as the following format: 

* **Paper Title** <br>
*Author(s)* <br>
Conference, Year. [[Paper]](link) [[Code]](link) [[Website]](link)

Papers

  • Minimal Criterion Coevolution: A New Approach to Open-Ended Search
    Jonathan C. Brant, Kenneth O. Stanley
    GECCO, 2017. [Paper] [Code]

  • Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions
    Rui Wang, Joel Lehman, Jeff Clune, Kenneth O. Stanley
    GECCO, 2019. [Paper] [Code] [Website]

  • Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions
    Rui Wang, Joel Lehman, Aditya Rawal, Jiale Zhi, Yulun Li, Jeff Clune, Kenneth O. Stanley
    ICML, 2020. [Paper] [Code] [Website]

  • Co-generation of game levels and game-playing agents
    Aaron Dharna, Julian Togelius, L.B.Soros
    AIIDE 2020. [Paper] [Code]

  • Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design
    Michael Dennis, Natasha Jaques, Eugene Vinitsky, Alexandre Bayen, Stuart Russell, Andrew Critch, Sergey Levine
    NeurIPS, 2020. [Paper] [Code] [Website]

  • Co-optimising Robot Morphology and Controller in a Simulated Open-Ended Environment
    Emma Hjellbrekke Stensby, Kai Olav Ellefsen, Kyrre Glette
    EvoStar 2021. [Paper] [Code]

  • Prioritized Level Replay
    Minqi Jiang, Edward Grefenstette, Tim Rocktäschel
    ICML, 2021. [Paper] [Code]

  • Replay-Guided Adversarial Environment Design
    Minqi Jiang*, Michael Dennis*, Jack Parker-Holder, Jakob Foerster, Edward Grefenstette, Tim Rocktäschel
    NeurIPS, 2021. [Paper] [Code]

  • Environment Generation for Zero-Shot Compositional Reinforcement Learning
    Izzeddin Gur, Natasha Jaques, Yingjie Miao, Jongwook Choi, Manoj Tiwari, Honglak Lee, Aleksandra Faust
    NeurIPS, 2021. [Paper]

  • MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research
    Mikayel Samvelyan, Robert Kirk, Vitaly Kurin, Jack Parker-Holder, Minqi Jiang, Eric Hambro, Fabio Petroni, Heinrich Küttler, Edward Grefenstette, Tim Rocktäschel
    NeurIPS, 2021. [Paper] [Code] [Website]

  • Open-Ended Learning Leads to Generally Capable Agents
    Open Ended Learning Team, Adam Stooke, Anuj Mahajan, Catarina Barros, Charlie Deck, Jakob Bauer, Jakub Sygnowski, Maja Trebacz, Max Jaderberg, Michael Mathieu, Nat McAleese, Nathalie Bradley-Schmieg, Nathaniel Wong, Nicolas Porcel, Roberta Raileanu, Steph Hughes-Fitt, Valentin Dalibard, Wojciech Marian Czarnecki
    arXiv, 2021. [Paper] [Website]

  • SPOTTER: Extending Symbolic Planning Operators through Targeted Reinforcement Learning
    Vasanth Sarathy, Daniel Kasenberg, Shivam Goel, Jivko Sinapov, Matthias Scheutz
    arXiv, 2021. [Paper] [Code]

  • EvoCraft: A New Challenge for Open-Endedness
    Djordje Grbic, Rasmus Berg Palm, Elias Najarro, Claire Glanois, Sebastian Risi
    EvoStar, 2021. [Paper] [Website]

  • Video Games as a Testbed for Open-Ended Phenomena
    Sam Earle; Julian Togelius; L. B. Soros
    IEEE Conference on Games, 2021. [Paper]

  • Open-ended search for environments and adapted agents using map-elites
    Emma Stensby Norstein, Kai Olav Ellefsen, Kyrre Glette
    EvoStar, 2022. [Paper] [Code]

  • Minimal Criterion Artist Collective
    Kai Arulkumaran; Thu Nguyen-Phuoc
    GECCO, 2022. [Paper] [Code]

  • Evolving Curricula with Regret-Based Environment Design
    Jack Parker-Holder*, Minqi Jiang*, Michael Dennis, Mikayel Samvelyan, Jakob Foerster, Edward Grefenstette, Tim Rocktäschel
    ICML, 2022. [Paper] [Code] [Demo]

  • Evolution through Large Models
    Joel Lehman, Jonathan Gordon, Shawn Jain, Kamal Ndousse, Cathy Yeh, Kenneth Stanley
    arXiv, 2022. [Paper] [Code]

  • RAPid-Learn: A Framework for Learning to Recover for Handling Novelties in Open-World Environments
    Shivam Goel, Yash Shukla, Vasanth Sarathy, Matthias Scheutz, Jivko Sinapov
    arXiv, 2022. [Paper] [Code]

  • Transfer Dynamics in Emergent Evolutionary Curricula
    Aaron Dharna, Amy K. Hoover, Julian Togelius, Lisa Soros
    IEEE Transactions on Games, 2022. [Paper] [Code]

  • Watts: Infrastructure for Open-Ended Learning
    Aaron Dharna, Charlie Summers, Rohin Dasari, Julian Togelius, Amy K. Hoover
    ALOE Workshop 2022 [Paper] [Code]

  • MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge
    Linxi Fan, Guanzhi Wang, Yunfan Jiang, Ajay Mandlekar, Yuncong Yang, Haoyi Zhu, Andrew Tang, De-An Huang, Yuke Zhu, Anima Anandkumar
    NeurIPS, 2022. [Paper] [Code] [Website]

  • Grounding Aleatoric Uncertainty in Unsupervised Environment Design
    Minqi Jiang, Michael Dennis, Jack Parker-Holder, Andrei Lupu, Heinrich Küttler, Edward Grefenstette, Tim Rocktäschel, Jakob Foerster
    NeurIPS 2022. [Paper]

  • Language and Culture Internalisation for Human-Like Autotelic AI
    Cédric Colas, Tristan Karch, Clément Moulin-Frier, Pierre-Yves Oudeyer
    Nature Machine Intelligence, 2022. [Paper] [Website]

  • Flow-Lenia: Towards open-ended evolution in cellular automata through mass conservation and parameter localization
    Erwan Plantec, Gautier Hamon, Mayalen Etcheverry, Pierre-Yves Oudeyer, Clément Moulin-Frier, Bert Wang-Chak Chan
    ALife 2023. [Paper]

  • MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning
    Mikayel Samvelyan, Akbir Khan, Michael Dennis, Minqi Jiang, Jack Parker-Holder, Jakob Foerster, Roberta Raileanu, Tim Rocktäschel
    ICLR, 2023. [Paper] [Website]

  • Powderworld: A Platform for Understanding Generalization via Rich Task Distributions
    Kevin Frans, Philip Isola
    ICLR, 2023. [Paper] [Website] [Code]

  • Human-Timescale Adaptation in an Open-Ended Task Space
    Adaptive Agent Team, Jakob Bauer, Kate Baumli, Satinder Baveja, Feryal Behbahani, Avishkar Bhoopchand, Nathalie Bradley-Schmieg, Michael Chang, Natalie Clay, Adrian Collister, Vibhavari Dasagi, Lucy Gonzalez, Karol Gregor, Edward Hughes, Sheleem Kashem, Maria Loks-Thompson, Hannah Openshaw, Jack Parker-Holder, Shreya Pathak, Nicolas Perez-Nieves, Nemanja Rakicevic, Tim Rocktäschel, Yannick Schroecker, Jakub Sygnowski, Karl Tuyls, Sarah York, Alexander Zacherl, Lei Zhang
    ICML, 2023. [Paper] [Website]

  • Deep Laplacian-based Options for Temporally-Extended Exploration
    Martin Klissarov, Marlos C. Machado
    ICML, 2023. [Paper] [Blogpost 1] [Blogpost2]

  • Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design
    Matthew T. Jackson, Minqi Jiang, Jack Parker-Holder, Risto Vuorio, Chris Lu, Gregory Farquhar, Shimon Whiteson, Jakob N. Foerster
    NeurIPS, 2023. [Paper] [Code]

  • Voyager: An Open-Ended Embodied Agent with Large Language Models
    Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, Anima Anandkumar
    arXiv, 2023. [Paper] [Code] [Website]

  • OMNI: Open-endedness via Models of human Notions of Interestingness
    Jenny Zhang, Joel Lehman, Kenneth Stanley, Jeff Clune
    arXiv, 2023. [Paper] [Code] [Website]

  • Augmenting Autotelic Agents with Large Language Models
    Cédric Colas, Laetitia Teodorescu, Pierre-Yves Oudeyer, Xingdi Yuan, Marc-Alexandre Côté
    arXiv, 2023. [Paper]

  • Reward-Free Curricula for Training Robust World Models
    Marc Rigter, Minqi Jiang, Ingmar Posner
    arXiv, 2023. [Paper]

  • Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution
    Chrisantha Fernando, Dylan Banarse, Henryk Michalewski, Simon Osindero, Tim Rocktäschel
    arXiv, 2023. [Paper]

  • Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation
    Eric Zelikman, Eliana Lorch, Lester Mackey, Adam Tauman Kalai
    arXiv, 2023. [Paper]

  • Motif: Intrinsic Motivation from Artificial Intelligence Feedback
    Martin Klissarov, Pierluca D'Oro, Shagun Sodhani, Roberta Raileanu, Pierre-Luc Bacon, Pascal Vincent, Amy Zhang, Mikael Henaff
    arXiv, 2023. [Paper] [Code]

  • Quality-Diversity through AI Feedback
    Herbie Bradley, Andrew Dai, Hannah Teufel, Jenny Zhang, Koen Oostermeijer, Marco Bellagente, Jeff Clune, Kenneth Stanley, Grégory Schott, Joel Lehman
    arXiv, 2023. [Paper] [Website]

  • Quality Diversity through Human Feedback
    Li Ding, Jenny Zhang, Jeff Clune, Lee Spector, Joel Lehman
    arXiv, 2023. [Paper]

  • Eureka: Human-Level Reward Design via Coding Large Language Models
    Yecheng Jason Ma, William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke Zhu, Linxi Fan, Anima Anandkumar
    arXiv, 2023. [Paper] [Code] [Website]

  • OS-Copilot: Towards Generalist Computer Agents with Self-Improvement
    Zhiyong Wu, Chengcheng Han*, Zichen Ding, Zhenmin Weng, Zhoumianze Liu, Shunyu Yao, Tao Yu, Lingpeng Kong
    arXiv, 2024. [Paper] [Code] [Website]

Open-Ended AI Safety

  • Open Questions in Creating Safe Open-ended AI: Tensions Between Control and Creativity
    Adrien Ecoffet, Jeff Clune, Joel Lehman
    arXiv, 2020. [Paper]

Surveys and Perspectives on Open-Endedness

  • Why Greatness Cannot Be Planned: The Myth of the Objective
    Kenneth O. Stanley, Joel Lehman
    Springer, 2015. [Book]

  • Open-endedness: The last grand challenge you’ve never heard of
    Kenneth O. Stanley, Joel Lehman, Lisa Soros
    O'Reilly Radar, 2017. [Paper]

  • AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence
    Jeff Clune
    arXiv, 2019. [Paper]

  • Creative Problem Solving in Artificially Intelligent Agents: A Survey and Framework
    Evana Gizzi, Lakshmi Nair, Sonia Chernova, Jivko Sinapov
    arXiv, 2022. [Paper]

  • Executive Function: A Contrastive Value Policy for Resampling and Relabeling Perceptions via Hindsight Summarization?
    Chris Lengerich, Ben Lengerich.
    arXiv, 2022. [Paper]

  • General Intelligence Requires Rethinking Exploration
    Minqi Jiang, Tim Rocktäschel, Edward Grefenstette
    Royal Society Open Science, 2023. [Paper]