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Recent developments have established the vulnerability of deep Reinforcement Learning (RL) to policy manipulation attacks via adversarial perturbations.
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 1998
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Gradient descent for general reinforcement learning
Leemon C Baird III and Andrew W Moore · 1999
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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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
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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cleverhans v1. 0.0: an adversarial machine learning library
Nicolas Papernot, Ian Goodfellow, Ryan Sheatsley, Reuben Feinman, and Patrick McDaniel · 2016
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Practical black-box attacks against deep learning systems using adversarial examples
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami · 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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Alphago: Mastering the ancient game of go with machine learning
David Silver and Demis Hassabis · 2016
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Learning deep control policies for autonomous aerial vehicles with mpc-guided policy search
Tianhao Zhang, Gregory Kahn, Sergey Levine, and Pieter Abbeel · 2016
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The Next Generation Intelligent Transportation System: Connected, Safe and Green
Ribal Atallah · 2017
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Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks
Vahid Behzadan and Arslan Munir · 2017
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Crafting adversarial example attacks on policy learners
Vahid Behzadan · 2017
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Deep direct reinforcement learning for financial signal representation and trading
Yue Deng, Feng Bao, Youyong Kong, Zhiquan Ren, and Qionghai Dai · 2017
Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
Shixiang Gu, Ethan Holly, Timothy Lillicrap, and Sergey Levine · 2017
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Adversarial attacks on neural network policies
Sandy Huang, Nicolas Papernot, Ian Goodfellow, Yan Duan, and Pieter Abbeel · 2017
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Delving into adversarial attacks on deep policies
Jernej Kos and Dawn Song · 2017
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Detecting adversarial attacks on neural network policies with visual foresight
Yen-Chen Lin, Ming-Yu Liu, Min Sun, and Jia-Bin Huang · 2017
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Adversarially robust policy learning: Active construction of physically-plausible perturbations
Ajay Mandlekar, Yuke Zhu, Animesh Garg, Li Fei-Fei, and Silvio Savarese · 2017
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Openai baselines
Prafulla Dhariwal, Christopher Hesse, Matthias Plappert, Alec Radford, John Schulman, Szymon Sidor, and Yuhuai Wu · 2017
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Noisy networks for exploration
Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot, Jacob Menick, Ian Osband, Alex Graves, Vlad Mnih, Remi Munos, Demis Hassabis, Olivier Pietquin, et al · 2017
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Semi-supervised deep reinforcement learning in support of iot and smart city services
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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
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Target-driven visual navigation in indoor scenes using deep reinforcement learning
Yuke Zhu, Roozbeh Mottaghi, Eric Kolve, Joseph J Lim, Abhinav Gupta, Li Fei-Fei, and Ali Farhadi · 2017
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