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Open-ended algorithms aim to learn new, interesting behaviors forever.
Jacobellis v. Ohio, 378 U.S. 184
Potter Stewart · 1964
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‘Improving ratings’: audit in the British University system
Marilyn Strathern · 1997
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Planning and acting in partially observable stochastic domains
Leslie Pack Kaelbling, Michael L Littman, and Anthony R Cassandra · 1998
Earlier work this paper cites.
OPTICS: Ordering points to identify the clustering structure
Mihael Ankerst, Markus M Breunig, Hans-Peter Kriegel, and Jörg Sander · 1999
Earlier work this paper cites.
Existential risks: Analyzing human extinction scenarios and related hazards
Nick Bostrom · 2002
Earlier work this paper cites.
Intrinsic motivation systems for autonomous mental development
Pierre-Yves Oudeyer, Frdric Kaplan, and Verena V Hafner · 2007
Earlier work this paper cites.
Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
Earlier work this paper cites.
Evolving CPPNs to grow three-dimensional physical structures
Joshua E Auerbach and Josh C Bongard · 2010
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Novelty-based multiobjectivization
Jean-Baptiste Mouret · 2011
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Beyond open-endedness: Quantifying impressiveness
Joel Lehman and Kenneth O Stanley · 2012
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Active learning of inverse models with intrinsically motivated goal exploration in robots
Adrien Baranes and Pierre-Yves Oudeyer · 2013
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Illuminating search spaces by mapping elites
Jean-Baptiste Mouret and Jeff Clune · 2015
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Innovation engines: Automated creativity and improved stochastic optimization via deep learning
Anh Mai Nguyen, Jason Yosinski, and Jeff Clune · 2015
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High-dimensional continuous control using generalized advantage estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel · 2015
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Why Greatness Cannot Be Planned: The Myth of the Objective
Kenneth O. Stanley and Joel Lehman · 2015
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Unifying count-based exploration and intrinsic motivation
Marc Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, and Remi Munos · 2016
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Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
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Gate-variants of gated recurrent unit (GRU) neural networks
Rahul Dey and Fathi M Salem · 2017
Earlier work this paper cites.
Automated curriculum learning for neural networks
Alex Graves, Marc G Bellemare, Jacob Menick, Remi Munos, and Koray Kavukcuoglu · 2017
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AI2-thor: An interactive 3d environment for visual AI
Eric Kolve, Roozbeh Mottaghi, Winson Han, Eli VanderBilt, Luca Weihs, Alvaro Herrasti, Matt Deitke, Kiana Ehsani, Daniel Gordon, Yuke Zhu, et al · 2017
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Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A Efros, and Trevor Darrell · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Babyai: A platform to study the sample efficiency of grounded language learning
Maxime Chevalier-Boisvert, Dzmitry Bahdanau, Salem Lahlou, Lucas Willems, Chitwan Saharia, Thien Huu Nguyen, and Yoshua Bengio · 2018
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Diversity is all you need: Learning skills without a reward function
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine · 2018
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Automatic goal generation for reinforcement learning agents
Carlos Florensa, David Held, Xinyang Geng, and Pieter Abbeel · 2018
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Randomized prior functions for deep reinforcement learning
Ian Osband, John Aslanides, and Albin Cassirer · 2018
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Film: Visual reasoning with a general conditioning layer
Ethan Perez, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
Earlier work this paper cites.
Solving rubik’s cube with a robot hand
Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Alex Paino, Matthias Plappert, Glenn Powell, Raphael Ribas, et al · 2019
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A survey on intrinsic motivation in reinforcement learning
Arthur Aubret, Laetitia Matignon, and Salima Hassas · 2019
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Jeff Clune · 2019
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Go-explore: a new approach for hard-exploration problems
Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O Stanley, and Jeff Clune · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova · 2019
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Asymmetric self-play for automatic goal discovery in robotic manipulation
OpenAI OpenAI, Matthias Plappert, Raul Sampedro, Tao Xu, Ilge Akkaya, Vineet Kosaraju, Peter Welinder, Ruben D’Sa, Arthur Petron, Henrique P d O Pinto, et al · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
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Skill induction and planning with latent language
Pratyusha Sharma, Antonio Torralba, and Jacob Andreas · 2021
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Do as i can, not as i say: Grounding language in robotic affordances
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Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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Teacher–student curriculum learning
Tambet Matiisen, Avital Oliver, Taco Cohen, and John Schulman · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych · 2019
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Rui Wang, Joel Lehman, Jeff Clune, and Kenneth O Stanley · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Learning with amigo: Adversarially motivated intrinsic goals
Andres Campero, Roberta Raileanu, Heinrich Küttler, Joshua B Tenenbaum, Tim Rocktäschel, and Edward Grefenstette · 2020
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AI research considerations for human existential safety (ARCHES)
Andrew Critch and David Krueger · 2020
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Emergent complexity and zero-shot transfer via unsupervised environment design
Michael Dennis, Natasha Jaques, Eugene Vinitsky, Alexandre Bayen, Stuart Russell, Andrew Critch, and Sergey Levine · 2020
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Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, et al · 2022
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Constitutional AI: Harmlessness from AI feedback
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al · 2022
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Autotelic agents with intrinsically motivated goal-conditioned reinforcement learning: a short survey
Cédric Colas, Tristan Karch, Olivier Sigaud, and Pierre-Yves Oudeyer · 2022
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Transfer dynamics in emergent evolutionary curricula
Aaron Dharna, Amy K Hoover, Julian Togelius, and Lisa Soros · 2022
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Housekeep: Tidying virtual households using commonsense reasoning
Yash Kant, Arun Ramachandran, Sriram Yenamandra, Igor Gilitschenski, Dhruv Batra, Andrew Szot, and Harsh Agrawal · 2022
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Grimgep: learning progress for robust goal sampling in visual deep reinforcement learning
Grgur Kovač, Adrien Laversanne-Finot, and Pierre-Yves Oudeyer · 2022
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A systematic investigation of commonsense knowledge in large language models
Xiang Lorraine Li, Adhiguna Kuncoro, Jordan Hoffmann, Cyprien de Masson d’Autume, Phil Blunsom, and Aida Nematzadeh · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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Quality-Diversity through AI Feedback
Herbie Bradley, Andrew Dai, Hannah Teufel, Jenny Zhang, Koen Oostermeijer, Marco Bellagente, Jeff Clune, Kenneth Stanley, Grégory Schott, and Joel Lehman · 2023
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Open-world multi-task control through goal-aware representation learning and adaptive horizon prediction
Shaofei Cai, Zihao Wang, Xiaojian Ma, Anji Liu, and Yitao Liang · 2023
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Augmenting Autotelic Agents with Large Language Models
Cédric Colas, Laetitia Teodorescu, Pierre-Yves Oudeyer, Xingdi Yuan, and Marc-Alexandre Côté · 2023
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Quality Diversity through Human Feedback
Li Ding, Jenny Zhang, Jeff Clune, Lee Spector, and Joel Lehman · 2023
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Palm-e: An embodied multimodal language model
Danny Driess, Fei Xia, Mehdi SM Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, et al · 2023
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Guiding Pretraining in Reinforcement Learning with Large Language Models
Yuqing Du, Olivia Watkins, Zihan Wang, Cédric Colas, Trevor Darrell, Pieter Abbeel, Abhishek Gupta, and Jacob Andreas · 2023
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Reward design with language models
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Alan: Autonomously exploring robotic agents in the real world
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OpenAI · 2023
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Open-endedness: The Last Grand Challenge You’ve Never Heard Of
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Perceptive Locomotion with Controllable Pace and Natural Gait Transitions Over Uneven Terrains
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Human-Timescale Adaptation in an Open-Ended Task Space
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Foundation models for decision making: Problems, methods, and opportunities
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Language to Rewards for Robotic Skill Synthesis
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