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Evaluating the worst-case performance of a reinforcement learning (RL) agent under the strongest/optimal adversarial perturbations on state observations (within some constraints) is crucial for understanding the robustness of RL agents.
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Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Human-level control through deep reinforcement learning
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Deep reinforcement learning with double q-learning
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Vulnerability of deep reinforcement learning to policy induction attacks
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Lerrel Pinto, James Davidson, Rahul Sukthankar, and Abhinav Gupta · 2017
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John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Scalable trust-region method for deep reinforcement learning using kronecker-factored approximation
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Pytorch implementations of reinforcement learning algorithms
Ilya Kostrikov · 2018
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Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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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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Snooping attacks on deep reinforcement learning
Matthew Inkawhich, Yiran Chen, and Hai Li · 2020
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Nesterov momentum adversarial perturbations in the deep reinforcement learning domain
Ezgi Korkmaz · 2020
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Certified adversarial robustness for deep reinforcement learning
Björn Lütjens, Michael Everett, and Jonathan P How · 2020
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Robust deep reinforcement learning through adversarial loss
Tuomas Oikarinen, Tsui-Wei Weng, and Luca Daniel · 2020
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Policy teaching via environment poisoning: Training-time adversarial attacks against reinforcement learning
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The value function polytope in reinforcement learning
Robert Dadashi, Adrien Ali Taiga, Nicolas Le Roux, Dale Schuurmans, and Marc G. Bellemare · 2019
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Marc Fischer, Matthew Mirman, Steven Stalder, and Martin Vechev · 2019
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Robust deep reinforcement learning against adversarial perturbations on state observations
Huan Zhang, Hongge Chen, Chaowei Xiao, Bo Li, Mingyan Liu, Duane Boning, and Cho-Jui Hsieh
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Robustifying reinforcement learning agents via action space adversarial training
Kai Liang Tan, Yasaman Esfandiari, Xian Yeow Lee, Soumik Sarkar, et al · 2020
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Query-based targeted action-space adversarial policies on deep reinforcement learning agents
Xian Yeow Lee, Yasaman Esfandiari, Kai Liang Tan, and Soumik Sarkar · 2021
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Optimal attacks on reinforcement learning policies
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Vulnerability-aware poisoning mechanism for online rl with unknown dynamics
Yanchao Sun, Da Huo, and Furong Huang · 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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