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Deep neuroevolution and deep reinforcement learning (deep RL) algorithms are two popular approaches to policy search.
The Baldwin effect
George Gaylord Simpson · 1953
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Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Thomas Back · 1996
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Estimation of distribution algorithms: A new tool for evolutionary computation , volume 2
Pedro Larrañaga and Jose A Lozano · 2001
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Efficient natural evolution strategies
Yi Sun, Daan Wierstra, Tom Schaul, and Juergen Schmidhuber · 2009
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Information-geometric optimization algorithms: A unifying picture via invariance principles
L. Arnold, A. Auger, N. Hansen, and Y. Ollivier · 2011
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Robot skill learning: From reinforcement learning to evolution strategies
Freek Stulp and Olivier Sigaud · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
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Trust region policy optimization
John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, and Pieter Abbeel · 2015
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Benchmarking deep reinforcement learning for continuous control
Yan Duan, Xi Chen, Rein Houthooft, John Schulman, and Pieter Abbeel · 2016
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The cma evolution strategy: A tutorial
Nikolaus Hansen · 2016
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Deep exploration via bootstrapped dqn
Ian Osband, Charles Blundell, Alexander Pritzel, and Benjamin Van Roy · 2016
Cited alongside, same era.
Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Tim Salimans and Diederik P. Kingma · 2016
Cited alongside, same era.
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
Cited alongside, same era.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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GEP-PG: Decoupling exploration and exploitation in deep reinforcement learning algorithms
Cédric Colas, Olivier Sigaud, and Pierre-Yves Oudeyer · 2018
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Neural Architecture Search: A Survey
T. Elsken, J. Hendrik Metzen, and F. Hutter · 2018
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Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke van Hoof, and Dave Meger · 2018
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Meta-reinforcement learning of structured exploration strategies
Abhishek Gupta, Russell Mendonca, YuXuan Liu, Pieter Abbeel, and Sergey Levine · 2018
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Edoardo Conti, Vashisht Madhavan, Felipe Petroski Such, Joel Lehman, Kenneth O. Stanley, and Jeff Clune · 2017
Cited alongside, same era.
OpenAI baselines
Prafulla Dhariwal, Christopher Hesse, Oleg Klimov, Alex Nichol, Matthias Plappert, Alec Radford, John Schulman, Szymon Sidor, and Yuhuai Wu · 2017
Cited alongside, same era.
Deep reinforcement learning that matters
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2017
Cited alongside, same era.
Felipe Petroski Such, Vashisht Madhavan, Edoardo Conti, Joel Lehman, Kenneth O. Stanley, and Jeff Clune · 2017
Cited alongside, same era.
Parameter space noise for exploration
Matthias Plappert, Rein Houthooft, Prafulla Dhariwal, Szymon Sidor, Richard Y. Chen, Xi Chen, Tamim Asfour, Pieter Abbeel, and Marcin Andrychowicz · 2017
Cited alongside, same era.
Evolution strategies as a scalable alternative to reinforcement learning
Tim Salimans, Jonathan Ho, Xi Chen, and Ilya Sutskever · 2017
Cited alongside, same era.
Evolutionary reinforcement learning
Shauharda Khadka and Kagan Tumer
Cited in the paper.
Evolution-guided policy gradient in reinforcement learning
Shauharda Khadka and Kagan Tumer
Cited in the paper.
Closest in time.
Rein Houthooft, Richard Y Chen, Phillip Isola, Bradly C Stadie, Filip Wolski, Jonathan Ho, and Pieter Abbeel · 2018
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Guided evolutionary strategies: escaping the curse of dimensionality in random search
Niru Maheswaranathan, Luke Metz, George Tucker, and Jascha Sohl-Dickstein · 2018
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Importance mixing: Improving sample reuse in evolutionary policy search methods
Aloïs Pourchot, Nicolas Perrin, and Olivier Sigaud · 2018
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Policy search in continuous action domains: an overview
Olivier Sigaud and Freek Stulp · 2018
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2018
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