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Recent studies reveal that a well-trained deep reinforcement learning (RL) policy can be particularly vulnerable to adversarial perturbations on input observations.
Recursive stochastic algorithms for global optimization in rˆd
Saul B Gelfand and Sanjoy K Mitter · 1991
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Consideration of risk in reinforcement learning
Matthias Heger · 1994
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Reinforcement learning under circumstances beyond its control
Chris Gaskett · 2003
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Reinforcement learning in robust markov decision processes
Shiau Hong Lim, Huan Xu, and Shie Mannor · 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 Riedmiller · 2013
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Scaling up robust mdps by reinforcement learning
Aviv Tamar, Huan Xu, and Shie Mannor · 2013
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A comprehensive survey on safe reinforcement learning
Javier Garcıa and Fernando Fernández · 2015
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Deep reinforcement learning with double q-learning
Arthur Guez and H Van Hasselt · 2015
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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 · 2015
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Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver · 2015
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Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
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Deep reinforcement learning with double q-learning
David Silver Hado Van Hasselt, Arthur Guez · 2016
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Dueling network architectures for deep reinforcement learning
Ziyu Wang, Tom Schaul, Matteo Hessel, Hado Hasselt, Marc Lanctot, and Nando Freitas · 2016
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Vulnerability of deep reinforcement learning to policy induction attacks
Vahid Behzadan and Arslan Munir · 2017
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Whatever does not kill deep reinforcement learning, makes it stronger
Vahid Behzadan and Arslan Munir · 2017
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Tom B Brown, Dandelion Mané, Aurko Roy, Martín Abadi, and Justin Gilmer · 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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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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Robust deep reinforcement learning with adversarial attacks
Anay Pattanaik, Zhenyi Tang, Shuijing Liu, Gautham Bommannan, and Girish Chowdhary · 2017
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Robust adversarial reinforcement learning
Lerrel Pinto, James Davidson, Rahul Sukthankar, and Abhinav Gupta · 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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Threat of adversarial attacks on deep learning in computer vision: A survey
Naveed Akhtar and Ajmal Mian · 2018
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Adversarial attacks and defences: A survey
Anirban Chakraborty, Manaar Alam, Vishal Dey, Anupam Chattopadhyay, and Debdeep Mukhopadhyay · 2018
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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 reinforcement learning for continuous control with model misspecification
Daniel J. Mankowitz, Nir Levine, Rae Jeong, Abbas Abdolmaleki, Jost Tobias Springenberg, Yuanyuan Shi, Jackie Kay, Todd Hester, Timothy Mann, and Martin Riedmiller · 2020
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Policy teaching via environment poisoning: Training-time adversarial attacks against reinforcement learning
Amin Rakhsha, Goran Radanovic, Rati Devidze, Xiaojin Zhu, and Adish Singla · 2020
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Deep reinforcement learning with robust and smooth policy
Qianli Shen, Yan Li, Haoming Jiang, Zhaoran Wang, and Tuo Zhao · 2020
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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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On the effectiveness of interval bound propagation for training verifiably robust models
Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Chongli Qin, Jonathan Uesato, Relja Arandjelovic, Timothy Mann, and Pushmeet Kohli · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Differentiable abstract interpretation for provably robust neural networks
Matthew Mirman, Timon Gehr, and Martin Vechev · 2018
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Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong and Zico Kolter · 2018
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Efficient neural network robustness certification with general activation functions
Huan Zhang, Tsui-Wei Weng, Pin-Yu Chen, Cho-Jui Hsieh, and Luca Daniel · 2018
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Certified adversarial robustness via randomized smoothing
Jeremy Cohen, Elan Rosenfeld, and Zico Kolter · 2019
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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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Automatic perturbation analysis for scalable certified robustness and beyond
Kaidi Xu, Zhouxing Shi, Huan Zhang, Yihan Wang, Kai-Wei Chang, Minlie Huang, Bhavya Kailkhura, Xue Lin, and Cho-Jui Hsieh · 2020
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Towards stable and efficient training of verifiably robust neural networks
Huan Zhang, Hongge Chen, Chaowei Xiao, Sven Gowal, Robert Stanforth, Bo Li, Duane Boning, and Cho-Jui Hsieh · 2020
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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 · 2020
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Adaptive reward-poisoning attacks against reinforcement learning
Xuezhou Zhang, Yuzhe Ma, Adish Singla, and Xiaojin Zhu · 2020
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Investigating vulnerabilities of deep neural policies
Ezgi Korkmaz · 2021
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Policy smoothing for provably robust reinforcement learning
Aounon Kumar, Alexander Levine, and Soheil Feizi · 2021
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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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Robust deep reinforcement learning through adversarial loss
Tuomas Oikarinen, Wang Zhang, Alexandre Megretski, Luca Daniel, and Tsui-Wei Weng · 2021
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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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Who is the strongest enemy? towards optimal and efficient evasion attacks in deep rl
Yanchao Sun, Ruijie Zheng, Yongyuan Liang, and Furong Huang · 2021
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Safe reinforcement learning by imagining the near future
Garrett Thomas, Yuping Luo, and Tengyu Ma · 2021
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Crop: Certifying robust policies for reinforcement learning through functional smoothing
Fan Wu, Linyi Li, Zijian Huang, Yevgeniy Vorobeychik, Ding Zhao, and Bo Li · 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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