Fetching the paper…
Reading the bibliography…
Deep reinforcement learning (RL) policies are known to be vulnerable to adversarial perturbations to their observations, similar to adversarial examples for classifiers.
Stochastic games
Lloyd S. Shapley · 1953
Earlier work this paper cites.
On the principle of total evidence
I.J. Good · 1967
Earlier work this paper cites.
Nonlinear games: examples and counterexamples
John Doyle, James A. Primbs, Benjamin Shapiro, and Vesna Nevistić · 1996
Earlier work this paper cites.
Solving uncertain Markov decision processes
J. Andrew Bagnell, Andrew Y. Ng, and Jeff G. Schneider · 2001
Earlier work this paper cites.
Visualizing data using t-SNE
Laurens van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus · 2014
Earlier work this paper cites.
Scaling up robust MDPs using function approximation
Aviv Tamar, Shie Mannor, and Huan Xu · 2014
Earlier work this paper cites.
Robust controller synthesis of switched systems using counterexample guided framework
Hadi Ravanbakhsh and Sriram Sankaranarayanan · 2016
Earlier work this paper cites.
CARLA: An open urban driving simulator
Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun · 2017
Earlier work this paper cites.
Attacking machine learning with adversarial examples
Ian Goodfellow, Nicolas Papernot, Sandy Huang, Yan Duan, Pieter Abbeel, and Jack Clark · 2017
Earlier work this paper cites.
Adversarial attacks on neural network policies
Sandy H. Huang, Nicolas Papernot, Ian J. Goodfellow, Yan Duan, and Pieter Abbeel · 2017
Earlier work this paper cites.
Delving into adversarial attacks on deep policies
Jernej Kos and Dawn Song · 2017
Cited alongside, same era.
A unified game-theoretic approach to multiagent reinforcement learning
Marc Lanctot, Vinicius Zambaldi, Audrunas Gruslys, Angeliki Lazaridou, Karl Tuyls, Julien Perolat, David Silver, and Thore Graepel · 2017
Cited alongside, same era.
Deal or no deal? End-to-end learning of negotiation dialogues
Mike Lewis, Denis Yarats, Yann Dauphin, Devi Parikh, and Dhruv Batra · 2017
Cited alongside, same era.
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
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.
OpenAI Five
OpenAI · 2018
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.
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, and Demis Hassabis · 2018
Later among the works it cites.
Adversarial risk and the dangers of evaluating against weak attacks
Jonathan Uesato, Brendan O’Donoghue, Pushmeet Kohli, and Aaron van den Oord · 2018
Later among the works it cites.
Open-ended learning in symmetric zero-sum games
David Balduzzi, Marta Garnelo, Yoram Bachrach, Wojciech M. Czarnecki, Julien Pérolat, Max Jaderberg, and Thore Graepel · 2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
JPMorgan develops robot to execute trades
Laura Noonan · 2017
Cited alongside, same era.
Robust adversarial reinforcement learning
Lerrel Pinto, James Davidson, Rahul Sukthankar, and Abhinav Gupta · 2017
Cited alongside, same era.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Cited alongside, same era.
Verifiable reinforcement learning via policy extraction
Osbert Bastani, Yewen Pu, and Armando Solar-Lezama · 2018
Cited alongside, same era.
Verifying controllers against adversarial examples with Bayesian optimization
Shromona Ghosh, Felix Berkenkamp, Gireeja Ranade, Shaz Qadeer, and Ashish Kapoor · 2018
Cited alongside, same era.
On the geometry of adversarial examples
Marc Khoury and Dylan Hadfield-Menell · 2018
Cited alongside, same era.
Emergent complexity via multi-agent competition
Trapit Bansal, Jakub Pachocki, Szymon Sidor, Ilya Sutskever, and Igor Mordatch
Cited in the paper.
Adversarial reinforcement learning framework for benchmarking collision avoidance mechanisms in autonomous vehicles
Vahid Behzadan and Arslan Munir · 2019
Closest in time.
Deep counterfactual regret minimization
Noam Brown, Adam Lerer, Sam Gross, and Tuomas Sandholm · 2019
Closest in time.
Stable Baselines
Ashley Hill, Antonin Raffin, Maximilian Ernestus, Adam Gleave, Anssi Kanervisto, Rene Traore, Prafulla Dhariwal, Christopher Hesse, Oleg Klimov, Alex Nichol, Matthias Plappert, Alec Radford, John Schulman, Szymon Sidor, and Yuhuai Wu · 2019
Closest in time.
Adversarial examples are not bugs, they are features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry · 2019
Closest in time.
Are adversarial examples inevitable?
Ali Shafahi, W. Ronny Huang, Christoph Studer, Soheil Feizi, and Tom Goldstein · 2019
Closest in time.
Feature denoising for improving adversarial robustness
Cihang Xie, Yuxin Wu, Laurens van der Maaten, Alan Yuille, and Kaiming He · 2019
Closest in time.