How You Act Tells a Lot: Privacy-Leakage Attack on Deep Reinforcement Learning

Handbook of genetic algorithms

Lawrence Davis. 1991 · 1991

Earlier work this paper cites.

A niched Pareto genetic algorithm for multiobjective optimization. In Proceedings of the first IEEE conference on evolutionary computation, IEEE world congress on computational intelligence

Jeffrey Horn, Nicholas Nafpliotis, and David E Goldberg. 1994 · 1994

Earlier work this paper cites.

Preventing premature convergence to local optima in genetic algorithms via random offspring generation. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems

Miguel Rocha and José Neves. 1999 · 1999

Earlier work this paper cites.

A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In International conference on parallel problem solving from nature

Kalyanmoy Deb, Samir Agrawal, Amrit Pratap, and Tanaka Meyarivan. 2000 · 2000

Earlier work this paper cites.

Apprenticeship learning via inverse reinforcement learning. In Proceedings of the twenty-first international conference on Machine learning

Pieter Abbeel and Andrew Y Ng. 2004 · 2004

Earlier work this paper cites.

Privacy-preserving reinforcement learning. In Proceedings of the 25th international conference on Machine learning

Jun Sakuma, Shigenobu Kobayashi, and Rebecca N Wright. 2008 · 2008

Earlier work this paper cites.

Maximum Entropy Inverse Reinforcement Learning.. In AAAI

Original

Brian D Ziebart, Andrew L Maas, J Andrew Bagnell, and Anind K Dey. 2008 · 2008

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.

Privacy-preserving deep learning. In Proceedings of the 22nd ACM SIGSAC conference on computer and communications security

Reza Shokri and Vitaly Shmatikov. 2015 · 2015

Earlier work this paper cites.

Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security

Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016 · 2016

Earlier work this paper cites.

Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016

Earlier work this paper cites.

Inverse reinforcement learning with simultaneous estimation of rewards and dynamics. In Artificial Intelligence and Statistics

Michael Herman, Tobias Gindele, Jörg Wagner, Felix Schmitt, and Wolfram Burgard. 2016 · 2016

Cited alongside, same era.

Continuous control with deep reinforcement learning. In International Conference on Learning Representation (ICLR)

Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. 2016 · 2016

Cited alongside, same era.

Learning to navigate in complex environments

Original

Piotr Mirowski, Razvan Pascanu, Fabio Viola, Hubert Soyer, Andrew J Ballard, Andrea Banino, Misha Denil, Ross Goroshin, Laurent Sifre, Koray Kavukcuoglu, et al · 2016

Cited alongside, same era.

Value iteration networks. In Advances in Neural Information Processing Systems

Aviv Tamar, Yi Wu, Garrett Thomas, Sergey Levine, and Pieter Abbeel. 2016 · 2016

Cited alongside, same era.

Neural optimizer search with reinforcement learning. In Proceedings of the 34th International Conference on Machine Learning-Volume 70

Membership inference attacks against machine learning models. In 2017 IEEE Symposium on Security and Privacy (SP)

Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2017 · 2017

Later among the works it cites.

Machine learning models that remember too much. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security

Congzheng Song, Thomas Ristenpart, and Vitaly Shmatikov. 2017 · 2017

Later among the works it cites.

Deep neuroevolution: genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning

Original

Felipe Petroski Such, Vashisht Madhavan, Edoardo Conti, Joel Lehman, Kenneth O Stanley, and Jeff Clune. 2017 · 2017

Later among the works it cites.

Attention is all you need. In Advances in Neural Information Processing Systems

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017

Later among the works it cites.