Fetching the paper…
Reading the bibliography…
Reinforcement learning (RL) has advanced greatly in the past few years with the employment of effective deep neural networks (DNNs) on the policy networks.
A Markovian decision process
Richard Bellman. 1957 · 1957
Earlier work this paper cites.
Dynamic programming and optimal control . Vol. 1
Dimitri P Bertsekas, Dimitri P Bertsekas, Dimitri P Bertsekas, and Dimitri P Bertsekas. 1995 · 1995
Earlier work this paper cites.
Td-gammon: A self-teaching backgammon program
Gerald Tesauro. 1995 · 1995
Earlier work this paper cites.
A Robust Minimax Approach to Classification
Gert R.G. Lanckriet, Laurent El Ghaoui, Chiranjib Bhattacharyya, and Michael I. Jordan. 2003 · 2003
Earlier work this paper cites.
Reinforcement learning for humanoid robotics. In Proceedings of the third IEEE-RAS international conference on humanoid robots . 1–20
Jan Peters, Sethu Vijayakumar, and Stefan Schaal. 2003 · 2003
Earlier work this paper cites.
Dueling Network Architectures for Deep Reinforcement Learning. In International Conference on Machine Learning . 1995–2003
Ziyu Wang, Tom Schaul, Matteo Hessel, Hado Hasselt, Marc Lanctot, and Nando Freitas. 2016 · 2003
Earlier work this paper cites.
Adversarial learning. In Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining . ACM, 641–647
Daniel Lowd and Christopher Meek. 2005 · 2005
Earlier work this paper cites.
Feature weighting for improved classifier robustness. In CEAS’09: sixth conference on email and anti-spam
Aleksander Kołcz and Choon Hui Teo. 2009 · 2009
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems . 1097–1105
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012 · 2012
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Intriguing properties of neural networks. In ICLR
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. 2014 · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples. In ICLR
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015 · 2015
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.
Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver. 2015 · 2015
Cited alongside, same era.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. 2016 · 2016
Cited alongside, same era.
Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio. 2016 · 2016
Cited alongside, same era.
Deepfool: a simple and accurate method to fool deep neural networks. In CVPR
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard. 2016 · 2016
Cited alongside, same era.
Optimal medication dosing from suboptimal clinical examples: A deep reinforcement learning approach. In Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the . IEEE, 2978–2981
Hacking Sensors
Yongdae Kim. 2017 · 2017
Later among the works it cites.
Adversarial examples for generative models
Jernej Kos, Ian Fischer, and Dawn Song. 2017 · 2017
Later among the works it cites.
Delving into adversarial attacks on deep policies
Jernej Kos and Dawn Song. 2017 · 2017
Later among the works it cites.
Detecting Adversarial Attacks on Neural Network Policies with Visual Foresight
Yen-Chen Lin, Ming-Yu Liu, Min Sun, and Jia-Bin Huang. 2017 · 2017
Later among the works it cites.
Adversarially robust policy learning: Active construction of physically-plausible perturbations. In IEEE International Conference on Intelligent Robots and Systems (to appear)
Ajay Mandlekar, Yuke Zhu, Animesh Garg, Li Fei-Fei, and Silvio Savarese. 2017 · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Shamim Nemati, Mohammad M Ghassemi, and Gari D Clifford. 2016 · 2016
Cited alongside, same era.
Mastering the game of Go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
Cited alongside, same era.
Deep Reinforcement Learning with Double Q-Learning
Hado Van Hasselt, Arthur Guez, and David Silver. 2016 · 2016
Cited alongside, same era.
Vulnerability of deep reinforcement learning to policy induction attacks. In International Conference on Machine Learning and Data Mining in Pattern Recognition . Springer, 262–275
Vahid Behzadan and Arslan Munir. 2017 · 2017
Cited alongside, same era.
OpenAI Baselines
Prafulla Dhariwal, Christopher Hesse, Oleg Klimov, Alex Nichol, Matthias Plappert, Alec Radford, John Schulman, Szymon Sidor, and Yuhuai Wu. 2017 · 2017
Cited alongside, same era.
Discovering Adversarial Examples with Momentum
Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Xiaolin Hu, and Jun Zhu. 2017 · 2017
Cited alongside, same era.
Dhiraj Gandhi, Lerrel Pinto, and Abhinav Gupta. 2017 · 2017
Cited alongside, same era.
Adversarial Attacks on Neural Network Policies
Sandy H. Huang, Nicolas Papernot, Ian J. Goodfellow, Yan Duan, and Pieter Abbeel. 2017 · 2017
Cited alongside, same era.
Universal adversarial perturbations. In CVPR
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard. 2017 · 2017
Later among the works it cites.
Adversarial Image Perturbation for Privacy Protection – A Game Theory Perspective. In International Conference on Computer Vision (ICCV)
Seong Joon Oh, Mario Fritz, and Bernt Schiele. 2017 · 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. 2017 · 2017
Later among the works it cites.
Robust Adversarial Reinforcement Learning. In International Conference on Machine Learning . 2817–2826
Lerrel Pinto, James Davidson, Rahul Sukthankar, and Abhinav Gupta. 2017 · 2017
Later among the works it cites.
Deep reinforcement learning framework for autonomous driving
Ahmad EL Sallab, Mohammed Abdou, Etienne Perot, and Senthil Yogamani. 2017 · 2017
Later among the works it cites.
Adversarial examples for semantic segmentation and object detection. In International Conference on Computer Vision. IEEE
Cihang Xie, Jianyu Wang, Zhishuai Zhang, Yuyin Zhou, Lingxi Xie, and Alan Yuille. 2017 · 2017
Later among the works it cites.
Learning to Attack: Adversarial Transformation Networks. In Proceedings of AAAI-2018
Shumeet Baluja and Ian Fischer. 2018 · 2018
Closest in time.