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Data poisoning for reinforcement learning has historically focused on general performance degradation, and targeted attacks have been successful via perturbations that involve control of the victim's policy and rewards.
Deep reinforcement learning for cyber security
Thanh Thi Nguyen and Vijay Janapa Reddi · 1906
Earlier work this paper cites.
The security of machine learning
Marco Barreno, Blaine Nelson, Anthony D. Joseph, and J. D. Tygar · 2010
Earlier work this paper cites.
Poisoning Attacks against Support Vector Machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
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The arcade learning environment: An evaluation platform for general agents
M. G. Bellemare, Y. Naddaf, J. Veness, and M. Bowling · 2013
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization
Luis Muñoz-González, Battista Biggio, Ambra Demontis, Andrea Paudice, Vasin Wongrassamee, Emil C. Lupu, and Fabio Roli · 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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Stable baselines
Ashley Hill, Antonin Raffin, Maximilian Ernestus, Adam Gleave, Rene Traore, Prafulla Dhariwal, Christopher Hesse, Oleg Klimov, Alex Nichol, Matthias Plappert, et al · 2018
Cited alongside, same era.
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
Cited alongside, same era.
Clean-label backdoor attacks
Alexander Turner, Dimitris Tsipras, and Aleksander Madry · 2018
Cited alongside, same era.
Learning to confuse: Generating training time adversarial data with auto-encoder
Ji Feng, Qi-Zhi Cai, and Zhi-Hua Zhou · 2019
Cited alongside, same era.
Simulation-based reinforcement learning for autonomous driving
Christopher Galias, Adam Jakubowski, Henryk Michalewski, and Błazej Osiński · 2019
Cited alongside, same era.
Trojdrl: Trojan attacks on deep reinforcement learning agents, 2019
Panagiota Kiourti, Kacper Wardega, Susmit Jha, and Wenchao Li · 2019
Cited alongside, same era.
Challenges of Reinforcement Learning , pages 249–272
Hao Dong, Zihan Ding, and Shanghang Zhang, editors · 2020
Later among the works it cites.
Witches’ brew: Industrial scale data poisoning via gradient matching, 2020
Jonas Geiping, Liam Fowl, W. Ronny Huang, Wojciech Czaja, Gavin Taylor, Michael Moeller, and Tom Goldstein · 2020
Later among the works it cites.
Dataset condensation with gradient matching
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2020
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Preventing unauthorized use of proprietary data: Poisoning for secure dataset release
Liam Fowl, Ping-yeh Chiang, Micah Goldblum, Jonas Geiping, Arpit Bansal, Wojtek Czaja, and Tom Goldstein · 2021
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Unlearnable examples: Making personal data unexploitable
Hanxun Huang, Xingjun Ma, Sarah Monazam Erfani, James Bailey, and Yisen Wang · 2021
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AlphaStar: Mastering the Real-Time Strategy Game StarCraft II
Oriol Vinyals, Igor Babuschkin, Junyoung Chung, Michael Mathieu, Max Jaderberg, Wojtek Czarnecki, Andrew Dudzik, Aja Huang, Petko Georgiev, Richard Powell, Timo Ewalds, Dan Horgan, Manuel Kroiss, Ivo Danihelka, John Agapiou, Junhyuk Oh, Valentin Dalibard, David Choi, Laurent Sifre, Yury Sulsky, Sasha Vezhnevets, James Molloy, Trevor Cai, David Budden, Tom Paine, Caglar Gulcehre, Ziyu Wang, Tobias Pfaff, Toby Pohlen, Dani Yogatama, Julia Cohen, Katrina McKinney, Oliver Smith, Tom Schaul, Timothy Lillicrap, Chris Apps, Koray Kavukcuoglu, Demis Hassabis, and David Silver · 2019
Cited alongside, same era.
Later among the works it cites.
Vulnerability-aware poisoning mechanism for online rl with unknown dynamics, 2021
Yanchao Sun, Da Huo, and Furong Huang · 2021
Later among the works it cites.