Fetching the paper…
Reading the bibliography…
Control policies, trained using the Deep Reinforcement Learning, have been recently shown to be vulnerable to adversarial attacks introducing even very small perturbations to the policy input.
Optimal control of markov processes with incomplete state information
Karl J Astrom · 1965
Earlier work this paper cites.
Learning without state-estimation in partially observable markovian decision processes
Satinder P Singh, Tommi Jaakkola, and Michael I Jordan · 1994
Earlier work this paper cites.
Robust reinforcement learning
Jun Morimoto and Kenji Doya · 2005
Earlier work this paper cites.
Partially observable markov decision processes
Matthijs TJ Spaan · 2012
Earlier work this paper cites.
Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
Earlier work this paper cites.
The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
Earlier work this paper cites.
Deterministic policy gradient algorithms
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin Riedmiller · 2014
Earlier work this paper cites.
Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
Earlier work this paper cites.
Deep recurrent q-learning for partially observable mdps
Matthew Hausknecht and Peter Stone · 2015
Cited alongside, same era.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Cited alongside, same era.
Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
Cited alongside, same era.
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 · 2016
Cited alongside, same era.
End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
Cited alongside, same era.
Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, and Demis Hassabis · 2017
Later among the works it cites.
Adversarial attacks on neural network policies
Sandy Huang, Nicolas Papernot, Ian Goodfellow, Yan Duan, and Pieter Abbeel · 2017
Later among the works it cites.
Vulnerability of deep reinforcement learning to policy induction attacks
Vahid Behzadan and Arslan Munir · 2017
Later among the works it 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
Later among the works it cites.
Delving into adversarial attacks on deep policies
Jernej Kos and Dawn Song · 2017
Later among the works it cites.
Robust adversarial reinforcement learning
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Cited alongside, same era.
keras-rl
Matthias Plappert · 2016
Cited alongside, same era.
Lerrel Pinto, James Davidson, Rahul Sukthankar, and Abhinav Gupta · 2017
Later among the works it cites.
Robust deep reinforcement learning with adversarial attacks
Anay Pattanaik, Zhenyi Tang, Shuijing Liu, Gautham Bommannan, and Girish Chowdhary · 2018
Later among the works it cites.