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We introduce two tactics to attack agents trained by deep reinforcement learning algorithms using adversarial examples, namely the strategically-timed attack and the enchanting attack.
Action-gap phenomenon in reinforcement learning
Amir massoud Farahmand · 2011
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Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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The cross-entropy method: a unified approach to combinatorial optimization, Monte-Carlo simulation and machine learning
Reuven Y Rubinstein and Dirk P Kroese · 2013
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Deep speech: Scaling up end-to-end speech recognition
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Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
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Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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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
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Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L Lewis, and Satinder Singh · 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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Nicholas Carlini and David Wagner · 2016
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Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio · 2016
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Deepfool: A simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
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The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z Berkay Celik, and Ananthram Swami · 2016
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Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
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Towards robust deep neural networks with BANG
Andras Rozsa, Manuel Günther, and Terrance E. Boult · 2016
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Improving the robustness of deep neural networks via stability training
Stephan Zheng, Yang Song, Thomas Leung, and Ian Goodfellow · 2016
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, and Mehdi Mirza · 2016
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Sandy Huang, Nicolas Papernot, Ian Goodfellow, Yan Duan, and Pieter Abbeel · 2017
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