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Evolution Strategies (ES) have recently been demonstrated to be a viable alternative to reinforcement learning (RL) algorithms on a set of challenging deep RL problems, including Atari games and MuJoCo humanoid locomotion benchmarks.
On a test of whether one of two random variables is stochastically larger than the other
Henry B Mann and Donald R Whitney · 1947
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Evolutionsstrategie–optimierung technisher systeme nach prinzipien der biologischen evolution
Ingo Rechenberg · 1973
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Adapting arbitrary normal mutation distributions in evolution strategies: The covariance matrix adaptation
Nikolaus Hansen and Andreas Ostermeier · 1996
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Convergence properties of evolutionary algorithms
Günter Rudolph · 1997
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Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cma-es)
Nikolaus Hansen, Sibylle D Müller, and Petros Koumoutsakos · 2003
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Uncertainty handling in model selection for support vector machines
Tobias Glasmachers and Christian Igel · 2008
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Accelerated neural evolution through cooperatively coevolved synapses
Faustino Gomez, Jürgen Schmidhuber, and Risto Miikkulainen · 2008
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Natural evolution strategies
Daan Wierstra, Tom Schaul, Jan Peters, and Juergen Schmidhuber · 2008
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Hoeffding and bernstein races for selecting policies in evolutionary direct policy search
Verena Heidrich-Meisner and Christian Igel · 2009
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Mirrored sampling and sequential selection for evolution strategies
Dimo Brockhoff, Anne Auger, Nikolaus Hansen, Dirk V Arnold, and Tim Hohm · 2010
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Evolutionary kernel learning
Christian Igel · 2011
Cited alongside, same era.
Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
Cited alongside, same era.
Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
Cited alongside, same era.
Robot skill learning: From reinforcement learning to evolution strategies
Freek Stulp and Olivier Sigaud · 2013
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Natural evolution strategies
Benchmarking deep reinforcement learning for continuous control
Yan Duan, Xi Chen, Rein Houthooft, John Schulman, and Pieter Abbeel · 2016
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CMA-ES for Hyperparameter Optimization of Deep Neural Networks
Ilya Loshchilov and Frank Hutter · 2016
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Edoardo Conti, Vashisht Madhavan, Felipe Petroski Such, Joel Lehman, Kenneth O Stanley, and Jeff Clune · 2017
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Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jan Peters, and Jürgen Schmidhuber · 2014
Cited alongside, same era.
Fast and accurate deep network learning by exponential linear units (elus)
Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Cited alongside, same era.
Openai baselines
Prafulla Dhariwal, Christopher Hesse, Matthias Plappert, Alec Radford, John Schulman, Szymon Sidor, and Yuhuai Wu · 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 reinforcement learning: An overview
Yuxi Li · 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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Felipe Petroski Such, Vashisht Madhavan, Edoardo Conti, Joel Lehman, Kenneth O Stanley, and Jeff Clune · 2017
Later among the works it cites.