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Optimizing functions without access to gradients is the remit of black-box methods such as evolution strategies.
Evolutionsstrategie
Ingo Rechenberg · 1973
Earlier work this paper cites.
Cognitron: A self-organizing multilayered neural network
Kunihiko Fukushima · 1975
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Evolutionsstrategien für die numerische optimierung
Hans-Paul Schwefel · 1977
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Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-… hook
Jürgen Schmidhuber · 1987
Earlier work this paper cites.
On the optimization of a synaptic learning rule
Samy Bengio, Yoshua Bengio, Jocelyn Cloutier, and Jan Gescei · 1992
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Pegasus: A policy search method for large mdps and pomdps
Andrew Y Ng and Michael I Jordan · 2000
Earlier work this paper cites.
Completely derandomized self-adaptation in evolution strategies
Nikolaus Hansen and Andreas Ostermeier · 2001
Earlier work this paper cites.
Evolution strategies–a comprehensive introduction
Hans-Georg Beyer and Hans-Paul Schwefel · 2002
Earlier work this paper cites.
Matplotlib: A 2d graphics environment
John D Hunter · 2007
Earlier work this paper cites.
A simple modification in cma-es achieving linear time and space complexity
Raymond Ros and Nikolaus Hansen · 2008
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Real-parameter black-box optimization benchmarking 2010: Experimental setup
Nikolaus Hansen, Anne Auger, Steffen Finck, and Raymond Ros · 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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A rigorous analysis of the harmony search algorithm: How the research community can be misled by a “novel” methodology
Dennis Weyland · 2010
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High dimensions and heavy tails for natural evolution strategies
Tom Schaul, Tobias Glasmachers, and Jürgen Schmidhuber · 2011
Earlier work this paper cites.
Natural evolution strategies
Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jan Peters, and Jürgen Schmidhuber · 2014
Earlier work this paper cites.
Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando De Freitas · 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
Earlier work this paper cites.
Learning to reinforcement learn
Jane X Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z Leibo, Remi Munos, Charles Blundell, Dharshan Kumaran, and Matt Botvinick · 2016
Earlier work this paper cites.
Learning to learn without gradient descent by gradient descent
Yutian Chen, Matthew W. Hoffman, Sergio Gómez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Matt Botvinick, and Nando de Freitas · 2017
Earlier work this paper cites.
Quality and diversity optimization: A unifying modular framework
Antoine Cully and Yiannis Demiris · 2017
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Population based training of neural networks
Max Jaderberg, Valentin Dalibard, Simon Osindero, Wojciech M Czarnecki, Jeff Donahue, Ali Razavi, Oriol Vinyals, Tim Green, Iain Dunning, Karen Simonyan, et al · 2017
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Random gradient-free minimization of convex functions
Yurii Nesterov and Vladimir Spokoiny · 2017
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Information-geometric optimization algorithms: A unifying picture via invariance principles
Yann Ollivier, Ludovic Arnold, Anne Auger, and Nikolaus Hansen · 2017
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Evolution strategies as a scalable alternative to reinforcement learning
Tim Salimans, Jonathan Ho, Xi Chen, Szymon Sidor, and Ilya Sutskever · 2017
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Linformer: Self-attention with linear complexity
Sinong Wang, Belinda Z Li, Madian Khabsa, Han Fang, and Hao Ma · 2020
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Meta-gradient reinforcement learning with an objective discovered online
Zhongwen Xu, Hado P van Hasselt, Matteo Hessel, Junhyuk Oh, Satinder Singh, and David Silver · 2020
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A self-tuning actor-critic algorithm
Tom Zahavy, Zhongwen Xu, Vivek Veeriah, Matteo Hessel, Junhyuk Oh, Hado P van Hasselt, David Silver, and Satinder Singh · 2020
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Sebastian Flennerhag, Yannick Schroecker, Tom Zahavy, Hado van Hasselt, David Silver, and Satinder Singh · 2021
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Brax–a differentiable physics engine for large scale rigid body simulation
C Daniel Freeman, Erik Frey, Anton Raichuk, Sertan Girgin, Igor Mordatch, and Olivier Bachem · 2021
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Felipe Petroski Such, Vashisht Madhavan, Edoardo Conti, Joel Lehman, Kenneth O Stanley, and Jeff Clune · 2017
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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
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Meta-gradient reinforcement learning
Zhongwen Xu, Hado P van Hasselt, and David Silver · 2018
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From complexity to simplicity: Adaptive es-active subspaces for blackbox optimization
Krzysztof M Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, and Vikas Sindhwani · 2019
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Metainit: Initializing learning by learning to initialize
Yann N Dauphin and Samuel Schoenholz · 2019
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Neural architecture search: A survey
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2019
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Improving generalization in meta reinforcement learning using learned objectives
Louis Kirsch, Sjoerd van Steenkiste, and Jürgen Schmidhuber · 2019
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Meta learning black-box population-based optimizers
Hugo Siqueira Gomes, Benjamin Léger, and Christian Gagné · 2021
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Perceiver: General perception with iterative attention
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Meta learning backpropagation and improving it
Louis Kirsch and Jürgen Schmidhuber · 2021
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Learn2hop: Learned optimization on rough landscapes
Amil Merchant, Luke Metz, Samuel S Schoenholz, and Ekin D Cubuk · 2021
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The sensory neuron as a transformer: Permutation-invariant neural networks for reinforcement learning
Yujin Tang and David Ha · 2021
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Unbiased gradient estimation in unrolled computation graphs with persistent evolution strategies
Paul Vicol, Luke Metz, and Jascha Sohl-Dickstein · 2021
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Seaborn: statistical data visualization
Michael L Waskom · 2021
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Self-referential meta learning
Louis Kirsch and Jürgen Schmidhuber · 2022
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Introducing symmetries to black box meta reinforcement learning
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Learning not to learn: Nature versus nurture in silico
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Pi-ars: Accelerating evolution-learned visual-locomotion with predictive information representations
Kuang-Huei Lee, Ofir Nachum, Tingnan Zhang, Sergio Guadarrama, Jie Tan, and Wenhao Yu · 2022
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Discovered policy optimisation
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Practical tradeoffs between memory, compute, and performance in learned optimizers
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Automated reinforcement learning (autorl): A survey and open problems
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Evojax: Hardware-accelerated neuroevolution
Yujin Tang, Yingtao Tian, and David Ha · 2022
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