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Adversarial attacks have exposed a significant security vulnerability in state-of-the-art machine learning models.
Dynamic programming
Richard Bellman · 1966
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Reinforcement learning: An introduction
Richard S. Sutton and Andrew G. Barto · 1988
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Technical note: Q-learning
Christopher J. C. H. Watkins and Peter Dayan · 1992
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The arcade learning environment: An evaluation platform for general agents
Marc G. Bellemare, Yavar Naddaf, Joel Veness, and Michael H. Bowling · 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 A. Riedmiller · 2013
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Deterministic policy gradient algorithms
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin A. Riedmiller · 2014
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus · 2014
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Fast r-cnn
Ross B. Girshick · 2015
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Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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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 A. Riedmiller, Andreas Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 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 P. Singh · 2015
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Trust region policy optimization
John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, and Pieter Abbeel · 2015
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End to end learning for self-driving cars
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D. Jackel, Miguel Pozuelo Monfort, Urs Muller, Jiakai Zhang, Xin Zhang, Junbo Jake Zhao, and Karol Zieba · 2016
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Openai gym, 2016
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
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Continuous control with deep reinforcement learning
Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2016
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick D. McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, and Ananthram Swami · 2016
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Practical black-box attacks against machine learning
Nicolas Papernot, Patrick D. McDaniel, Ian J. Goodfellow, Somesh Jha, Z. Berkay Celik, and Ananthram Swami · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Deep reinforcement learning framework for autonomous driving
Ahmad El Sallab, Mohammed Abdou, Etienne Perot, and Senthil Yogamani · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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The space of transferable adversarial examples
Florian Tramèr, Nicolas Papernot, Ian J. Goodfellow, Dan Boneh, and Patrick D. McDaniel · 2017
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Vedavyas Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy P. Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis · 2016
Cited alongside, same era.
Vulnerability of deep reinforcement learning to policy induction attacks
Vahid Behzadan and Arslan Munir · 2017
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David A. Wagner · 2017
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Openai baselines
Prafulla Dhariwal, Christopher Hesse, Oleg Klimov, Alex Nichol, Matthias Plappert, Alec Radford, John Schulman, Szymon Sidor, Yuhuai Wu, and Peter Zhokhov · 2017
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Adversarial attacks on neural network policies
Sandy H. Huang, Nicolas Papernot, Ian J. Goodfellow, Yan Duan, and Pieter Abbeel · 2017
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A deep learning approach for joint video frame and reward prediction in atari games
Felix Leibfried, Nate Kushman, and Katja Hofmann · 2017
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Tactics of adversarial attack on deep reinforcement learning agents
Yen-Chen Lin, Zhang-Wei Hong, Yuan-Hong Liao, Meng-Li Shih, Ming-Yu Liu, and Min Sun · 2017
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Deep action conditional neural network for frame prediction in atari games
Elias Wang · 2017
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Boosting adversarial attacks with momentum
Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li · 2018
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Visualizing and understanding atari agents
Sam Greydanus, Anurag Koul, Jonathan Dodge, and Alan Fern · 2018
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Pytorch implementations of reinforcement learning algorithms
Ilya Kostrikov · 2018
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Zero-shot visual imitation
Deepak Pathak, Parsa Mahmoudieh, Guanghao Luo, Pulkit Agrawal, Dian Chen, Yide Shentu, Evan Shelhamer, Jitendra Malik, Alexei A. Efros, and Trevor Darrell · 2018
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Robust deep reinforcement learning with adversarial attacks
Anay Pattanaik, Zhenyi Tang, Shuijing Liu, Gautham Bommannan, and Girish Chowdhary · 2018
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Asymmetric actor critic for image-based robot learning
Lerrel Pinto, Marcin Andrychowicz, Peter Welinder, Wojciech Zaremba, and Pieter Abbeel · 2018
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Feature space perturbations yield more transferable adversarial examples
Nathan Inkawhich, Wei Wen, Hai Li, and Yiran Chen · 2019
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