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While the history of machine learning so far largely encompasses a series of problems posed by researchers and algorithms that learn their solutions, an important question is whether the problems themselves can be generated by the algorithm at the same time as they are being solved.
Evolutionsstrategien
Ingo Rechenberg · 1978
Earlier work this paper cites.
An approach to the synthesis of life
Thomas S Ray · 1991
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Incremental evolution of complex general behavior
Faustino Gomez and Risto Miikkulainen · 1997
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Reinforcement learning: An introduction , volume 1
Richard S Sutton and Andrew G Barto · 1998
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Challenges in coevolutionary learning: Arms-race dynamics, open-endedness, and mediocre stable states
S.G. Ficici and J.B. Pollack · 1998
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An empirical analysis of collaboration methods in cooperative coevolutionary algorithms
R. Paul Wiegand, William C. Liles, and Kenneth A. De Jong · 2001
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Open-ended artificial evolution
Russell K Standish · 2003
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Pfeiffer – A distributed open-ended evolutionary system
W. B. Langdon · 2005
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Compositional pattern producing networks: A novel abstraction of development
Kenneth O. Stanley · 2007
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The arrow of complexity hypothesis (abstract)
Mark Bedau · 2008
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Exploiting open-endedness to solve problems through the search for novelty
Joel Lehman and Kenneth O. Stanley · 2008
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Natural evolution strategies
Daan Wierstra, Tom Schaul, Jan Peters, and Juergen Schmidhuber · 2008
Earlier work this paper cites.
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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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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A hypercube-based indirect encoding for evolving large-scale neural networks
Kenneth O. Stanley, David B. D’Ambrosio, and Jason Gauci · 2009
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Evolving coordinated quadruped gaits with the HyperNEAT generative encoding
Jeff Clune, Benjamin E. Beckmann, Charles Ofria, and Robert T. Pennock · 2009
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Towards directed open-ended search by a novelty guided evolution strategy
Lars Graening, Nikola Aulig, and Markus Olhofer · 2010
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Revising the evolutionary computation abstraction: minimal criteria novelty search
Joel Lehman and Kenneth O. Stanley · 2010
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Parameter-exploring policy gradients
Frank Sehnke, Christian Osendorfer, Thomas Rückstieß, Alex Graves, Jan Peters, and Jürgen Schmidhuber · 2010
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Search-based procedural content generation: A taxonomy and survey
Julian Togelius, Georgios N Yannakakis, Kenneth O Stanley, and Cameron Browne · 2011
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Novelty search and the problem with objectives
Joel Lehman and Kenneth O. Stanley · 2011
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On the performance of indirect encoding across the continuum of regularity
Jeff Clune, Kenneth O. Stanley, Robert T. Pennock, and Charles Ofria · 2011
Earlier work this paper cites.
Evolving robot gaits in hardware: the hyperneat generative encoding vs. parameter optimization
Jason Yosinski, Jeff Clune, Diana Hidalgo, Sarah Nguyen, Juan Cristobal Zagal, and Hod Lipson · 2011
Earlier work this paper cites.
Coevolutionary Principles , pages 987–1033
Elena Popovici, Anthony Bucci, R. Paul Wiegand, and Edwin D. De Jong · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Curriculum learning for motor skills
Andrej Karpathy and Michiel Van De Panne · 2012
Cited alongside, same era.
The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
Cited alongside, same era.
Powerplay: Training an increasingly general problem solver by continually searching for the simplest still unsolvable problem
Jürgen Schmidhuber · 2013
Cited alongside, same era.
Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding
N. Cheney, R. MacCurdy, J. Clune, and H. Lipson · 2013
Cited alongside, same era.
Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, and M. Bernstein et al · 2014
Cited alongside, same era.
Open-endedness: The last grand challenge you’ve never heard of
Kenneth O. Stanley, Joel Lehman, and Lisa Soros · 2017
Later among the works it cites.
Minimal criterion coevolution: A new approach to open-ended search
Jonathan C. Brant and Kenneth O. Stanley · 2017
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Reverse curriculum generation for reinforcement learning
Carlos Florensa, David Held, Markus Wulfmeier, Michael Zhang, and Pieter Abbeel · 2017
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Teacher-student curriculum learning
Tambet Matiisen, Avital Oliver, Taco Cohen, and John Schulman · 2017
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Evolution strategies as a scalable alternative to reinforcement learning
Tim Salimans, Jonathan Ho, Xi Chen, and Ilya Sutskever · 2017
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Identifying necessary conditions for open-ended evolution through the artificial life world of chromaria
L.B. Soros and Kenneth O Stanley · 2014
Cited alongside, same era.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Cited alongside, same era.
Illuminating search spaces by mapping elites
Jean-Baptiste Mouret and Jeff Clune · 2015
Cited alongside, same era.
Robots that can adapt like animals
A. Cully, J. Clune, D. Tarapore, and J.-B. Mouret · 2015
Cited alongside, same era.
Why greatness cannot be planned
Kenneth O Stanley and Joel Lehman · 2015
Cited alongside, same era.
Xingwen Zhang, Jeff Clune, and Kenneth O. Stanley · 2017
Later among the works it cites.
Evolving stable strategies
David Ha · 2017
Later among the works it cites.
Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
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Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures
Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Volodymir Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, et al · 2018
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Distributed prioritized experience replay
Dan Horgan, John Quan, David Budden, Gabriel Barth-Maron, Matteo Hessel, Hado Van Hasselt, and David Silver · 2018
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Multi-task deep reinforcement learning with popart
Matteo Hessel, Hubert Soyer, Lasse Espeholt, Wojciech Czarnecki, Simon Schmitt, and Hado van Hasselt · 2018
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Montezuma’s revenge solved by go-explore, a new algorithm for hard-exploration problems (sets records on pitfall, too)
Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O. Stanley, and Jeff Clune · 2018
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A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al · 2018
Later among the works it cites.
Emergent complexity via multi-agent competition
Trapit Bansal, Jakub Pachocki, Szymon Sidor, Ilya Sutskever, and Igor Mordatch · 2018
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Coevolutionary neural population models
Nick Moran and Jordan B. Pollack · 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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Illuminating generalization in deep reinforcement learning through procedural level generation
Niels Justesen, Ruben Rodriguez Torrado, Philip Bontrager, Ahmed Khalifa, Julian Togelius, and Sebastian Risi · 2018
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Joost Huizinga and Jeff Clune · 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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Episodic curiosity through reachability
Nikolay Savinov, Anton Raichuk, Raphael Marinier, Damien Vincent, Marc Pollefeys, Timothy Lillicrap, and Sylvain Gelly · 2018
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Es is more than just a traditional finite-difference approximator
Joel Lehman, Jay Chen, Jeff Clune, and Kenneth O. Stanley · 2018
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Improving exploration in evolution strategies for deep reinforcement learning via a population of novelty-seeking agents
Edoardo Conti, Vashisht Madhavan, Felipe Petroski Such, Joel Lehman, Kenneth Stanley, and Jeff Clune · 2018
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Reinforcement learning for improving agent design
David Ha · 2018
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Unsupervised meta-learning for reinforcement learning
Abhishek Gupta, Benjamin Eysenbach, Chelsea Finn, and Sergey Levine · 2018
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Recurrent experience replay in distributed reinforcement learning
Steven Kapturowski, Georg Ostrovski, Will Dabney, John Quan, and Remi Munos · 2019
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