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Because stochastic gradient descent (SGD) has shown promise optimizing neural networks with millions of parameters and few if any alternatives are known to exist, it has moved to the heart of leading approaches to reinforcement learning (RL).
Learning internal representations by error propagation
David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams · 1986
Earlier work this paper cites.
The MNIST database of handwritten digits, 1998
Yann LeCun and Corinna Cortes · 1998
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Evolving artificial neural networks
Xin Yao · 1999
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Evolution strategies: A comprehensive introduction
Hans-Georg Beyer and Hans-Paul Schwefel · 2002
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Evolutionary Computation: A Unified Perspective
Kenneth A. De Jong · 2002
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Evolving neural networks through augmenting topologies
Kenneth O. Stanley and Risto Miikkulainen · 2002
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Neuroevolution for reinforcement learning using evolution strategies
Christian Igel · 2003
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Efficient non-linear control through neuroevolution
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Reducing the dimensionality of data with neural networks
G.E. Hinton and R.R. Salakhutdinov · 2006
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Neuroevolution: from architectures to learning
Dario Floreano, Peter Dürr, and Claudio Mattiussi · 2008
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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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Autonomous evolution of topographic regularities in artificial neural networks
Jason Gauci and Kenneth O. Stanley · 2010
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Evolving static representations for task transfer
Phillip Verbancsics and Kenneth O. Stanley · 2010
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Natural evolution strategies
Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jan Peters, and Jürgen Schmidhuber · 2014
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Simple evolutionary optimization can rival stochastic gradient descent in neural networks
Gregory Morse and Kenneth O. Stanley · 2016
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Gradient-free policy architecture search and adaptation
Sayna Ebrahimi, Anna Rohrbach, and Trevor Darrell · 2017
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ES is more than just a traditional finite-difference approximator
Joel Lehman, Jay Chen, Jeff Clune, and Kenneth O. Stanley · 2017
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Deep neuroevolution: Genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning
Felipe Petroski Such, Vashisht Madhavan, Edoardo Conti, Joel Lehman, Kenneth O. Stanley, and Jeff Clune · 2017
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Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee W Teh · 2011
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Matthias Plappert, Rein Houthooft, Prafulla Dhariwal, Szymon Sidor, Richard Y Chen, Xi Chen, Tamim Asfour, Pieter Abbeel, and Marcin Andrychowicz · 2017
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Evolution Strategies as a Scalable Alternative to Reinforcement Learning
T. Salimans, J. Ho, X. Chen, S. Sidor, and I. Sutskever · 2017
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