Fetching the paper…
Reading the bibliography…
Deep reinforcement learning (DRL) policies have been shown to be deceived by perturbations (e.g., random noise or intensional adversarial attacks) on state observations that appear at test time but are unknown during training.
Improving generalization performance using double backpropagation
Harris Drucker and Yann Le Cun · 1992
Earlier work this paper cites.
Entropy and information theory
Robert M Gray · 2011
Earlier work this paper cites.
Reinforcement learning in robotics: Applications and real-world challenges
Petar Kormushev et al · 2013
Earlier work this paper cites.
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
Earlier work this paper cites.
Andrei A Rusu, Sergio Gomez Colmenarejo, Caglar Gulcehre, Guillaume Desjardins, James Kirkpatrick, Razvan Pascanu, Volodymyr Mnih, Koray Kavukcuoglu, and Raia Hadsell · 2015
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian Goodfellow et al · 2015
Earlier work this paper cites.
Dueling network architectures for deep reinforcement learning
Ziyu Wang, Tom Schaul, Matteo Hessel, Hado Hasselt, Marc Lanctot, and Nando Freitas · 2016
Earlier work this paper cites.
The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z Berkay Celik, and Ananthram Swami · 2016
Earlier work this paper cites.
Adversarial attacks on neural network policies
Sandy Huang, Nicolas Papernot, Ian Goodfellow, Yan Duan, and Pieter Abbeel · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Robust adversarial reinforcement learning
Lerrel Pinto, James Davidson, Rahul Sukthankar, and Abhinav Gupta · 2017
Cited alongside, same era.
Adversarially robust policy learning: Active construction of physically-plausible perturbations
Ajay Mandlekar, Yuke Zhu, Animesh Garg, Li Fei-Fei, and Silvio Savarese · 2017
Cited alongside, same era.
Constrained policy optimization
Joshua Achiam, David Held, Aviv Tamar, and Pieter Abbeel · 2017
Cited alongside, same era.
A distributional perspective on reinforcement learning
Marc G Bellemare, Will Dabney, and Rémi Munos · 2017
Cited alongside, same era.
Whatever does not kill deep reinforcement learning, makes it stronger
Vahid Behzadan and Arslan Munir · 2017
Cited alongside, same era.
Distilled agent dqn for provable adversarial robustness
Characterizing attacks on deep reinforcement learning
Chaowei Xiao, Xinlei Pan, Warren He, Jian Peng, Mingjie Sun, Jinfeng Yi, Bo Li, and Dawn Song · 2019
Later among the works it cites.
Online robustness training for deep reinforcement learning
Marc Fischer et al · 2019
Later among the works it cites.
Adversarial examples: Attacks and defenses for deep learning
Xiaoyong Yuan, Pan He, Qile Zhu, and Xiaolin Li · 2019
Later among the works it cites.
Distilling policy distillation
Wojciech M Czarnecki, Razvan Pascanu, Simon Osindero, Siddhant Jayakumar, Grzegorz Swirszcz, and Max Jaderberg · 2019
Later among the works it cites.
Robust learning with jacobian regularization
Judy Hoffman, Daniel A Roberts, and Sho Yaida · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Fischer Mirman et al · 2018
Cited alongside, same era.
Robust deep reinforcement learning with adversarial attacks
Anay Pattanaik, Zhenyi Tang, Shuijing Liu, Gautham Bommannan, and Girish Chowdhary · 2018
Cited alongside, same era.
Rainbow: Combining improvements in deep reinforcement learning
Matteo Hessel, Joseph Modayil, Hado Van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, and David Silver · 2018
Cited alongside, same era.
Noisy networks for exploration
Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot, Jacob Menick, Matteo Hessel, Ian Osband, Alex Graves, Volodymyr Mnih, Remi Munos, Demis Hassabis, et al · 2018
Cited alongside, same era.
Adversarial robustness toolbox v1.2.0
Maria-Irina Nicolae, Mathieu Sinn, Minh Ngoc Tran, Beat Buesser, Ambrish Rawat, Martin Wistuba, Valentina Zantedeschi, Nathalie Baracaldo, Bryant Chen, Heiko Ludwig, Ian Molloy, and Ben Edwards · 2018
Cited alongside, same era.
Jacobian adversarially regularized networks for robustness
Alvin Chan, Yi Tay, Yew Soon Ong, and Jie Fu · 2019
Later among the works it cites.
Scaleable input gradient regularization for adversarial robustness
Chris Finlay and Adam M Oberman · 2019
Later among the works it cites.
Toward a reinforcement learning environment toolbox for intelligent electric motor control
Arne Traue, Gerrit Book, Wilhelm Kirchgässner, and Oliver Wallscheid · 2020
Closest in time.
Minimalistic attacks: How little it takes to fool deep reinforcement learning policies
Xinghua Qu, Zhu Sun, Yew Soon Ong, Abhishek Gupta, and Pengfei Wei · 2020
Closest in time.
Robust deep reinforcement learning against adversarial perturbations on observations
Huan Zhang, Hongge Chen, Chaowei Xiao, Bo Li, Duane Boning, and Cho-Jui Hsieh · 2020
Closest in time.