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Quality-Diversity (QD) optimization algorithms are a well-known approach to generate large collections of diverse and high-quality solutions.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Z. Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 1912
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Using gpus for machine learning algorithms
Dave Steinkrau, Patrice Y. Simard, and Ian Buck · 2005
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Illuminating mario scenes in the latent space of a generative adversarial network
Matthew C Fontaine, Ruilin Liu, Ahmed Khalifa, Jignesh Modi, Julian Togelius, Amy K Hoover, and Stefanos Nikolaidis · 2007
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Overcoming the bootstrap problem in evolutionary robotics using behavioral diversity
Jean-Baptiste Mouret and Stéphane Doncieux · 2009
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Large-scale deep unsupervised learning using graphics processors
Rajat Raina, Anand Madhavan, and Andrew Y. Ng · 2009
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Comparing Results of 31 Algorithms from the Black-Box Optimization Benchmarking BBOB-2009
Nikolaus Hansen, Anne Auger, Raymond Ros, Steffen Finck, and Petr Posik · 2010
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SFERESv2: Evolvin’ in the multi-core world
J.-B. Mouret and S. Doncieux · 2010
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Torch7: A matlab-like environment for machine learning
Ronan Collobert, Koray Kavukcuoglu, and Clément Farabet · 2011
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Multi-column deep neural networks for image classification
Dan C. Ciresan, Ueli Meier, and Jürgen Schmidhuber · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Encouraging behavioral diversity in evolutionary robotics: An empirical study
J-B Mouret and Stéphane Doncieux · 2012
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Behavioral repertoire learning in robotics
Antoine Cully and Jean-Baptiste Mouret · 2013
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Robots that can adapt like animals
Antoine Cully, Jeff Clune, Danesh Tarapore, and Jean-Baptiste Mouret · 2015
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Deep learning
Yann Lecun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Illuminating search spaces by mapping elites
Jean-Baptiste Mouret and Jeff Clune · 2015
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Tensorflow: A system for large-scale machine learning
Martin Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Quality diversity: A new frontier for evolutionary computation
Justin K Pugh, Lisa B Soros, and Kenneth O Stanley · 2016
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You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi · 2016
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Quality and diversity optimization: A unifying modular framework
Antoine Cully and Yiannis Demiris · 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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Open-endedness: The last grand challenge you’ve never heard of
Kenneth O Stanley, Joel Lehman, and Lisa Soros · 2017
Cited alongside, same era.
Using centroidal voronoi tessellations to scale up the multidimensional archive of phenotypic elites algorithm
Vassilis Vassiliades, Konstantinos Chatzilygeroudis, and Jean-Baptiste Mouret · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
Cited alongside, same era.
Reset-free trial-and-error learning for robot damage recovery
Konstantinos Chatzilygeroudis, Vassilis Vassiliades, and Jean-Baptiste Mouret · 2018
Cited alongside, same era.
The carbon impact of artificial intelligence
Payal Dhar · 2020
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Covariance matrix adaptation for the rapid illumination of behavior space
Matthew C Fontaine, Julian Togelius, Stefanos Nikolaidis, and Amy K Hoover · 2020
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Adaptive prior selection for repertoire-based online adaptation in robotics
Rituraj Kaushik, Pierre Desreumaux, and Jean-Baptiste Mouret · 2020
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Model-based quality-diversity search for efficient robot learning
Leon Keller, Daniel Tanneberg, Svenja Stark, and Jan Peters · 2020
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Unsupervised learning and exploration of reachable outcome space
Giuseppe Paolo, Alban Laflaquiere, Alexandre Coninx, and Stephane Doncieux · 2020
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Green ai
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Cited alongside, same era.
Diversity is all you need: Learning skills without a reward function
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine · 2018
Cited alongside, same era.
Data-efficient design exploration through surrogate-assisted illumination
Adam Gaier, Alexander Asteroth, and Jean-Baptiste Mouret · 2018
Cited alongside, same era.
DART: Dynamic animation and robotics toolkit
Jeongseok Lee, Michael X. Grey, Sehoon Ha, Tobias Kunz, Sumit Jain, Yuting Ye, Siddhartha S. Srinivasa, Mike Stilman, and C. Karen Liu · 2018
Cited alongside, same era.
Discovering the Elite Hypervolume by Leveraging Interspecies Correlation
Vassilis Vassiliades and Jean-Baptiste Mouret · 2018
Cited alongside, same era.
Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
Andrei Barbu, David Mayo, Julian Alverio, William Luo, Christopher Wang, Danny Gutfreund, Joshua Tenenbaum, and Boris Katz · 2019
Cited alongside, same era.
Jeff Clune · 2019
Cited alongside, same era.
Roy Schwartz, Jesse Dodge, Noah A Smith, and Oren Etzioni · 2020
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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 Stanley · 2020
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Quality-diversity optimization: a novel branch of stochastic optimization
Konstantinos Chatzilygeroudis, Antoine Cully, Vassilis Vassiliades, and Jean-Baptiste Mouret · 2021
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Illuminating diverse neural cellular automata for level generation
Sam Earle, Justin Snider, Matthew C Fontaine, Stefanos Nikolaidis, and Julian Togelius · 2021
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First return, then explore
Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O Stanley, and Jeff Clune · 2021
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Differentiable quality diversity
Matthew Fontaine and Stefanos Nikolaidis · 2021
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Brax - a differentiable physics engine for large scale rigid body simulation, 2021
C. Daniel Freeman, Erik Frey, Anton Raichuk, Sertan Girgin, Igor Mordatch, and Olivier Bachem · 2021
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Shixiang Shane Gu, Manfred Diaz, Daniel C Freeman, Hiroki Furuta, Seyed Kamyar Seyed Ghasemipour, Anton Raichuk, Byron David, Erik Frey, Erwin Coumans, and Olivier Bachem · 2021
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Coco: A platform for comparing continuous optimizers in a black-box setting
Nikolaus Hansen, Anne Auger, Raymond Ros, Olaf Mersmann, Tea Tušar, and Dimo Brockhoff · 2021
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Highly accurate protein structure prediction with AlphaFold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A A Kohl, Andrew J Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W Senior, Koray Kavukcuoglu, Pushmeet Kohli, and Demis Hassabis · 2021
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Dynamics-aware quality-diversity for efficient learning of skill repertoires
Bryan Lim, Luca Grillotti, Lorenzo Bernasconi, and Antoine Cully · 2021
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Isaac gym: High performance gpu-based physics simulation for robot learning
Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo, Michelle Lu, Kier Storey, Miles Macklin, David Hoeller, Nikita Rudin, Arthur Allshire, Ankur Handa, and Gavriel State · 2021
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Policy gradient assisted map-elites
Olle Nilsson and Antoine Cully · 2021
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Diversity policy gradient for sample efficient quality-diversity optimization
Thomas Pierrot, Valentin Macé, Geoffrey Cideron, Karim Beguir, Antoine Cully, Olivier Sigaud, and Nicolas Perrin · 2021
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Learning to walk in minutes using massively parallel deep reinforcement learning
Nikita Rudin, David Hoeller, Philipp Reist, and Marco Hutter · 2021
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pyribs: A bare-bones python library for quality diversity optimization
Bryon Tjanaka, Matthew C. Fontaine, Yulun Zhang, Sam Sommerer, Nathan Dennler, and Stefanos Nikolaidis · 2021
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Using deepspeed and megatron to train megatron-turing nlg 530b, a large-scale generative language model, 2022
Shaden Smith, Mostofa Patwary, Brandon Norick, Patrick LeGresley, Samyam Rajbhandari, Jared Casper, Zhun Liu, Shrimai Prabhumoye, George Zerveas, Vijay Korthikanti, Elton Zhang, Rewon Child, Reza Yazdani Aminabadi, Julie Bernauer, Xia Song, Mohammad Shoeybi, Yuxiong He, Michael Houston, Saurabh Tiwary, and Bryan Catanzaro · 2022
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Approximating gradients for differentiable quality diversity in reinforcement learning
Bryon Tjanaka, Matthew C Fontaine, Julian Togelius, and Stefanos Nikolaidis · 2022
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