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Neuroevolution has proven effective at many reinforcement learning tasks, but does not seem to scale well to high-dimensional controller representations, which are needed for tasks where the input is raw pixel data.
Completely derandomized self-adaptation in evolution strategies
Nikolaus Hansen and Andreas Ostermeier · 2001
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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
Faustino Gomez, Jürgen Schmidhuber, and Risto Miikkulainen · 2006
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The cma evolution strategy: a comparing review
Nikolaus Hansen · 2006
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Reducing the dimensionality of data with neural networks
Geoffrey E Hinton and Ruslan R Salakhutdinov · 2006
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Neuroevolution: from architectures to learning
Dario Floreano, Peter Dürr, and Claudio Mattiussi · 2008
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Backpropagation without human supervision for visual control in quake ii
Matt Parker and Bobby D Bryant · 2009
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A hypercube-based encoding for evolving large-scale neural networks
Kenneth O Stanley, David B D’Ambrosio, and Jason Gauci · 2009
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Ontogenetic and phylogenetic reinforcement learning
Julian Togelius, Tom Schaul, Daan Wierstra, Christian Igel, Faustino Gomez, and Jürgen Schmidhuber · 2009
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Why does unsupervised pre-training help deep learning?
Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent, and Samy Bengio · 2010
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Deep auto-encoder neural networks in reinforcement learning
Sascha Lange and Martin Riedmiller · 2010
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Intrinsically motivated neuroevolution for vision-based reinforcement learning
Giuseppe Cuccu, Matthew Luciw, Jürgen Schmidhuber, and Faustino Gomez · 2011
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Stacked convolutional auto-encoders for hierarchical feature extraction
Jonathan Masci, Ueli Meier, Dan Cireşan, and Jürgen Schmidhuber · 2011
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Autonomous reinforcement learning on raw visual input data in a real world application
Sascha Lange, Martin Riedmiller, and Arne Voigtlander · 2012
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Evolving large-scale neural networks for vision-based reinforcement learning
Innovation engines: Automated creativity and improved stochastic optimization via deep learning
Anh Mai Nguyen, Jason Yosinski, and Jeff Clune · 2015
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Action-conditional video prediction using deep networks in atari games
Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L Lewis, and Satinder Singh · 2015
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Neuroevolution in games: State of the art and open challenges
Sebastian Risi and Julian Togelius · 2015
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Playing doom with slam-augmented deep reinforcement learning
Shehroze Bhatti, Alban Desmaison, Ondrej Miksik, Nantas Nardelli, N Siddharth, and Philip HS Torr · 2016
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Learning to act by predicting the future
Alexey Dosovitskiy and Vladlen Koltun · 2016
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Jan Koutník, Giuseppe Cuccu, Jürgen Schmidhuber, and Faustino Gomez · 2013
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Transforming exploratory creativity with delenox
Antonios Liapis, Héctor P Martınez, Julian Togelius, and Georgios N Yannakakis · 2013
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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Evolving deep unsupervised convolutional networks for vision-based reinforcement learning
Jan Koutník, Jürgen Schmidhuber, and Faustino Gomez · 2014
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Learning deep state representations with convolutional autoencoders
Gabriel Barth-Maron
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Vizdoom: A doom-based ai research platform for visual reinforcement learning
Michał Kempka, Marek Wydmuch, Grzegorz Runc, Jakub Toczek, and Wojciech Jaśkowski · 2016
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Playing fps games with deep reinforcement learning
Guillaume Lample and Devendra Singh Chaplot · 2016
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Risto Miikkulainen, Jason Liang, Elliot Meyerson, Aditya Rawal, Dan Fink, Olivier Francon, Bala Raju, Arshak Navruzyan, Nigel Duffy, and Babak Hodjat · 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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