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The study of provable adversarial robustness for deep neural networks (DNNs) has mainly focused on static supervised learning tasks such as image classification.
Neuronlike adaptive elements that can solve difficult learning control problems
Andrew G. Barto, Richard S. Sutton, and Charles W. Anderson · 1983
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Efficient memory-based learning for robot control
Andrew W. Moore · 1990
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On the Problem of the Most Efficient Tests of Statistical Hypotheses , pp. 73–108
J. Neyman and E. S. Pearson · 1992
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Robust deep reinforcement learning against adversarial perturbations on observations
Huan Zhang, Hongge Chen, Chaowei Xiao, Bo Li, Duane S. Boning, and Cho-Jui Hsieh · 2003
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Robust reinforcement learning using adversarial populations
Eugene Vinitsky, Yuqing Du, Kanaad Parvate, Kathy Jang, Pieter Abbeel, and Alexandre M. Bayen · 2008
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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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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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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 Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
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Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael I. Jordan, and Philipp Moritz · 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, Mathew Monfort, Urs Muller, Jiakai Zhang, Xin Zhang, 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 Manfred Otto 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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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
Earlier work this paper cites.
Vulnerability of deep reinforcement learning to policy induction attacks
Vahid Behzadan and Arslan Munir · 2017
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Adversarial and clean data are not twins
Zhitao Gong, Wenlu Wang, and Wei-Shinn Ku · 2017
Cited alongside, same era.
On the (statistical) detection of adversarial examples
Kathrin Grosse, Praveen Manoharan, Nicolas Papernot, Michael Backes, and Patrick D. McDaniel · 2017
Cited alongside, same era.
Adversarial attacks on neural network policies
Sandy H. Huang, Nicolas Papernot, Ian J. Goodfellow, Yan Duan, and Pieter Abbeel · 2017
Cited alongside, same era.
Adversarial machine learning at scale
Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio · 2017
Cited alongside, same era.
Adversarial examples detection in deep networks with convolutional filter statistics
Xin Li and Fuxin Li · 2017
Cited alongside, same era.
Achieving verified robustness to symbol substitutions via interval bound propagation
Po-Sen Huang, Robert Stanforth, Johannes Welbl, Chris Dyer, Dani Yogatama, Sven Gowal, Krishnamurthy Dvijotham, and Pushmeet Kohli · 2019
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Certified robustness to adversarial examples with differential privacy
Mathias Lécuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, and Suman Jana · 2019
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Certified adversarial robustness with additive noise
Bai Li, Changyou Chen, Wenlin Wang, and Lawrence Carin · 2019
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Certified adversarial robustness for deep reinforcement learning
Björn Lütjens, Michael Everett, and Jonathan P. How · 2019
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Stable baselines3
Antonin Raffin, Ashley Hill, Maximilian Ernestus, Adam Gleave, Anssi Kanervisto, and Noah Dormann · 2019
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Provably robust deep learning via adversarially trained smoothed classifiers
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David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy P. Lillicrap, Karen Simonyan, and Demis Hassabis · 2017
Cited alongside, same era.
Thermometer encoding: One hot way to resist adversarial examples
Jacob Buckman, Aurko Roy, Colin Raffel, and Ian J. Goodfellow · 2018
Cited alongside, same era.
Stochastic activation pruning for robust adversarial defense
Guneet S. Dhillon, Kamyar Azizzadenesheli, Zachary C. Lipton, Jeremy Bernstein, Jean Kossaifi, Aran Khanna, and Animashree Anandkumar · 2018
Cited alongside, same era.
Training verified learners with learned verifiers, 2018
Krishnamurthy Dvijotham, Sven Gowal, Robert Stanforth, Relja Arandjelovic, Brendan O’Donoghue, Jonathan Uesato, and Pushmeet Kohli · 2018
Cited alongside, same era.
On the effectiveness of interval bound propagation for training verifiably robust models, 2018
Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Chongli Qin, Jonathan Uesato, Relja Arandjelovic, Timothy Mann, and Pushmeet Kohli · 2018
Cited alongside, same era.
Countering adversarial images using input transformations
Chuan Guo, Mayank Rana, Moustapha Cissé, and Laurens van der Maaten · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Cited alongside, same era.
Hadi Salman, Jerry Li, Ilya P. Razenshteyn, Pengchuan Zhang, Huan Zhang, Sébastien Bubeck, and Greg Yang · 2019
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Robustness certificates against adversarial examples for relu networks
Sahil Singla and Soheil Feizi · 2019
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Certified defenses for adversarial patches
Ping-yeh Chiang, Renkun Ni, Ahmed Abdelkader, Chen Zhu, Christoph Studer, and Tom Goldstein · 2020
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Adversarial policies: Attacking deep reinforcement learning
Adam Gleave, Michael Dennis, Cody Wild, Neel Kant, Sergey Levine, and Stuart Russell · 2020
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Robust reinforcement learning via adversarial training with langevin dynamics
Parameswaran Kamalaruban, Yu-Ting Huang, Ya-Ping Hsieh, Paul Rolland, Cheng Shi, and Volkan Cevher · 2020
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Certifying confidence via randomized smoothing
Aounon Kumar, Alexander Levine, Soheil Feizi, and Tom Goldstein · 2020
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Rl baselines3 zoo
Antonin Raffin · 2020
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Second-order provable defenses against adversarial attacks, 2020
Sahil Singla and Soheil Feizi · 2020
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Center smoothing for certifiably robust vector-valued functions
Aounon Kumar and Tom Goldstein · 2021
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Improved, deterministic smoothing for L1 certified robustness
Alexander Levine and Soheil Feizi · 2021
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Sahil Singla and Soheil Feizi · 2021
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Robust reinforcement learning on state observations with learned optimal adversary
Huan Zhang, Hongge Chen, Duane S Boning, and Cho-Jui Hsieh · 2021
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