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We study locally differentially private algorithms for reinforcement learning to obtain a robust policy that performs well across distributed private environments.
Neuronlike adaptive elements that can solve difficult learning control problems
A. G. Barto, R. S. Sutton, and C. W. Anderson · 1983
Earlier work this paper cites.
Extensions of lipshitz mapping into hilbert space
W. B. Johnson · 1984
Earlier work this paper cites.
Database-friendly random projections
D. Achlioptas · 2001
Earlier work this paper cites.
Random projection in dimensionality reduction: Applications to image and text data
E. Bingham and H. Mannila · 2001
Earlier work this paper cites.
Cross channel optimized marketing by reinforcement learning
N. Abe, N. Verma, C. Apte, and R. Schroko · 2004
Earlier work this paper cites.
Robust reinforcement learning
J. Morimoto and K. Doya · 2005
Earlier work this paper cites.
Privacy preserving learning in negotiation
S. Zhang and F. Makedon · 2005
Earlier work this paper cites.
An approximate dynamic programming approach to decentralized control of stochastic systems
R. Cogill, M. Rotkowitz, B. Van Roy, and S. Lall · 2006
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
Earlier work this paper cites.
Privacy-preserving reinforcement learning
J. Sakuma, S. Kobayashi, and R. N. Wright · 2008
Earlier work this paper cites.
What can we learn privately?
S. P. Kasiviswanathan, H. K. Lee, K. Nissim, S. Raskhodnikova, and A. Smith · 2011
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Local privacy and statistical minimax rates
J. C. Duchi, M. I. Jordan, and M. J. Wainwright · 2013
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng · 2015
Cited alongside, same era.
Human-level control through deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al · 2015
Cited alongside, same era.
Massively parallel methods for deep reinforcement learning
A. Nair, P. Srinivasan, S. Blackwell, C. Alcicek, R. Fearon, A. De Maria, V. Panneershelvam, M. Suleyman, C. Beattie, S. Petersen, et al · 2015
Robust adversarial reinforcement learning
L. Pinto, J. Davidson, R. Sukthankar, and A. Gupta · 2017
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Comparing population means under local differential privacy: with significance and power
B. Ding, H. Nori, P. Li, and J. Allen · 2018
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Noisy networks for exploration
M. Fortunato, M. G. Azar, B. Piot, J. Menick, M. Hessel, I. Osband, A. Graves, V. Mnih, R. Munos, D. Hassabis, O. Pietquin, C. Blundell, and S. Legg · 2018
Later among the works it cites.
Microscopic traffic simulation by cooperative multi-agent deep reinforcement learning
G. Bacchiani, D. Molinari, and M. Patander · 2019
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Local differential privacy for deep learning
M. Chamikara, P. Bertok, I. Khalil, D. Liu, and S. Camtepe · 2019
Later among the works it cites.
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Openai gym, 2016
G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba · 2016
Cited alongside, same era.
Asynchronous methods for deep reinforcement learning
V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Harley, T. P. Lillicrap, D. Silver, and K. Kavukcuoglu · 2016
Cited alongside, same era.
Epopt: Learning robust neural network policies using model ensembles
A. Rajeswaran, S. Ghotra, B. Ravindran, and S. Levine · 2016
Cited alongside, same era.
Safe, multi-agent, reinforcement learning for autonomous driving
S. Shalev-Shwartz, S. Shammah, and A. Shashua · 2016
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Collecting telemetry data privately
B. Ding, J. Kulkarni, and S. Yekhanin · 2017
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G. Palmer, R. Savani, and K. Tuyls · 2019
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How you act tells a lot: Privacy-leaking attack on deep reinforcement learning
X. Pan, W. Wang, X. Zhang, B. Li, J. Yi, and D. Song · 2019
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Privacy-preserving q-learning with functional noise in continuous spaces
B. Wang and N. Hegde · 2019
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Collecting and analyzing multidimensional data with local differential privacy
N. Wang, X. Xiao, Y. Yang, J. Zhao, S. C. Hui, H. Shin, J. Shin, and G. Yu · 2019
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Applying differential privacy mechanism in artificial intelligence
T. Zhu and S. Y. Philip · 2019
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