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Deep reinforcement learning, applied to vision-based problems like Atari games, maps pixels directly to actions; internally, the deep neural network bears the responsibility of both extracting useful information and making decisions based on it.
Vector quantization
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Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition
Yagyensh Chandra Pati, Ramin Rezaiifar, and Perinkulam Sambamurthy Krishnaprasad · 1993
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Evolving artificial neural networks
Xin Yao · 1999
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Completely derandomized self-adaptation in evolution strategies
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Evolving neural networks through augmenting topologies
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Neuroevolution for reinforcement learning using evolution strategies
Christian Igel · 2003
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Neuroevolution: from architectures to learning
Dario Floreano, Peter Dürr, and Claudio Mattiussi · 2008
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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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Ontogenetic and phylogenetic reinforcement learning
Julian Togelius, Tom Schaul, Daan Wierstra, Christian Igel, Faustino Gomez, and Jürgen Schmidhuber · 2009
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Tobias Glasmachers, Tom Schaul, Sun Yi, Daan Wierstra, and Jürgen Schmidhuber · 2010
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High dimensions and heavy tails for natural evolution strategies
Tom Schaul, Tobias Glasmachers, and Jürgen Schmidhuber · 2011
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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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The importance of encoding versus training with sparse coding and vector quantization
Adam Coates and Andrew Y Ng · 2011
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Block diagonal natural evolution strategies
Giuseppe Cuccu and Faustino Gomez · 2012
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The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
Openai gym, 2016
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Rainbow: Combining improvements in deep reinforcement learning
Matteo Hessel, Joseph Modayil, Hado Van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, and David Silver · 2017
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Neuroevolution in games: State of the art and open challenges
Sebastian Risi and Julian Togelius · 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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Felipe Petroski Such, Vashisht Madhavan, Edoardo Conti, Joel Lehman, Kenneth O Stanley, and Jeff Clune · 2017
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