Fetching the paper…
Reading the bibliography…
We present a Quality-Diversity benchmark suite for Deep Neuroevolution in Reinforcement Learning domains for robot control.
Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition . Ieee, 248–255
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009 · 2009
Earlier work this paper cites.
Behavioral repertoire learning in robotics. In Proceedings of the 15th annual conference on Genetic and evolutionary computation . 175–182
Antoine Cully and Jean-Baptiste Mouret. 2013 · 2013
Earlier work this paper cites.
Microsoft coco: Common objects in context. In European conference on computer vision . Springer, 740–755
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014 · 2014
Earlier work this paper cites.
Robots that can adapt like animals
Antoine Cully, Jeff Clune, Danesh Tarapore, and Jean-Baptiste Mouret. 2015 · 2015
Earlier work this paper cites.
Illuminating search spaces by mapping elites
Jean-Baptiste Mouret and Jeff Clune. 2015 · 2015
Earlier work this paper cites.
Librispeech: an asr corpus based on public domain audio books. In 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP) . IEEE, 5206–5210
Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur. 2015 · 2015
Earlier work this paper cites.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. 2016 · 2016
Earlier work this paper cites.
Quality diversity: A new frontier for evolutionary computation
Justin K Pugh, Lisa B Soros, and Kenneth O Stanley. 2016 · 2016
Earlier work this paper cites.
Quality and diversity optimization: A unifying modular framework
Antoine Cully and Yiannis Demiris. 2017 · 2017
Earlier work this paper cites.
Evolution strategies as a scalable alternative to reinforcement learning
Tim Salimans, Jonathan Ho, Xi Chen, Szymon Sidor, and Ilya Sutskever. 2017 · 2017
Earlier work this paper cites.
Felipe Petroski Such, Vashisht Madhavan, Edoardo Conti, Joel Lehman, Kenneth O Stanley, and Jeff Clune. 2017 · 2017
Earlier work this paper cites.
Using centroidal voronoi tessellations to scale up the multidimensional archive of phenotypic elites algorithm
Vassilis Vassiliades, Konstantinos Chatzilygeroudis, and Jean-Baptiste Mouret. 2017 · 2017
Cited alongside, same era.
Reset-free trial-and-error learning for robot damage recovery
Konstantinos Chatzilygeroudis, Vassilis Vassiliades, and Jean-Baptiste Mouret. 2018 · 2018
Cited alongside, same era.
Data-efficient design exploration through surrogate-assisted illumination
Adam Gaier, Alexander Asteroth, and Jean-Baptiste Mouret. 2018 · 2018
Cited alongside, same era.
Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdolmaleki, Josh Merel, Andrew Lefrancq, et al · 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, Dan Gutfreund, Josh Tenenbaum, and Boris Katz. 2019 · 2019
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. 2020 · 2020
Later among the works it cites.
Discovering representations for black-box optimization. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference . 103–111
Adam Gaier, Alexander Asteroth, and Jean-Baptiste Mouret. 2020 · 2020
Later among the works it cites.
Quality-Diversity Optimization: a novel branch of stochastic optimization
Konstantinos Chatzilygeroudis, Antoine Cully, Vassilis Vassiliades, and Jean-Baptiste Mouret. 2021 · 2021
Later among the works it cites.
First return, then explore
Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O Stanley, and Jeff Clune. 2021 · 2021
Later among the works it cites.
Differentiable Quality Diversity
Matthew Fontaine and Stefanos Nikolaidis. 2021 · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Mapping hearthstone deck spaces through map-elites with sliding boundaries. In Proceedings of The Genetic and Evolutionary Computation Conference . 161–169
Matthew C Fontaine, Scott Lee, Lisa B Soros, Fernando de Mesentier Silva, Julian Togelius, and Amy K Hoover. 2019 · 2019
Cited alongside, same era.
Map-elites for noisy domains by adaptive sampling. In Proceedings of the Genetic and Evolutionary Computation Conference Companion . 121–122
Niels Justesen, Sebastian Risi, and Jean-Baptiste Mouret. 2019 · 2019
Cited alongside, same era.
Deep neuroevolution of recurrent and discrete world models. In Proceedings of the Genetic and Evolutionary Computation Conference . 456–462
Sebastian Risi and Kenneth O Stanley. 2019 · 2019
Cited alongside, same era.
Scaling map-elites to deep neuroevolution. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference . 67–75
Cédric Colas, Vashisht Madhavan, Joost Huizinga, and Jeff Clune. 2020 · 2020
Cited alongside, same era.
Fast and stable MAP-Elites in noisy domains using deep grids
Manon Flageat and Antoine Cully. 2020 · 2020
Cited alongside, same era.
C Daniel Freeman, Erik Frey, Anton Raichuk, Sertan Girgin, Igor Mordatch, and Olivier Bachem. 2021 · 2021
Later among the works it cites.
Policy gradient assisted MAP-Elites. In Proceedings of the Genetic and Evolutionary Computation Conference . 866–875
Olle Nilsson and Antoine Cully. 2021 · 2021
Later among the works it cites.
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 · 2021
Later among the works it cites.
Accelerated Quality-Diversity for Robotics through Massive Parallelism
Bryan Lim, Maxime Allard, Luca Grillotti, and Antoine Cully. 2022 · 2022
Closest in time.
Approximating Gradients for Differentiable Quality Diversity in Reinforcement Learning
Bryon Tjanaka, Matthew C Fontaine, Julian Togelius, and Stefanos Nikolaidis. 2022 · 2022
Closest in time.