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This paper proposes a framework called Watts for implementing, comparing, and recombining open-ended learning (OEL) algorithms.
OPEN-ENDED ARTIFICIAL EVOLUTION
RUSSELL K. STANDISH · 2003
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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How the strictness of the minimal criterion impacts open-ended evolution
L. B. Soros, Nick Cheney, and Kenneth O. Stanley · 2016
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Minimal criterion coevolution: a new approach to open-ended search
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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, Jeffrey Clune, and Kenneth O. Stanley · 2020
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Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Eric Liang, Melih Elibol, Zongheng Yang, William Paul, Michael I. Jordan, and Ion Stoica · 2018
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Necessary Conditions for Open-Ended Evolution
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Replay-guided adversarial environment design, 2021a
Minqi Jiang, Michael Dennis, Jack Parker-Holder, Jakob Foerster, Edward Grefenstette, and Tim Rocktäschel
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Open-ended learning leads to generally capable agents, 2021
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, and Wojciech Marian Czarnecki · 2021
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Yuqing Du, Pieter Abbeel, and Aditya Grover · 2022
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