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While deep reinforcement learning has successfully solved many challenging control tasks, its real-world applicability has been limited by the inability to ensure the safety of learned policies.
Neuronlike adaptive elements that can solve difficult learning control problems
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Russ Tedrake, Ian R Manchester, Mark Tobenkin, and John W Roberts · 2010
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Sergey Levine and Vladlen Koltun · 2013
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Matteo Turchetta, Felix Berkenkamp, and Andreas Krause · 2016
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Genesim: genetic extraction of a single, interpretable model
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Osbert Bastani, Carolyn Kim, and Hamsa Bastani · 2017
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Safe model-based reinforcement learning with stability guarantees
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A comprehensive survey on safe reinforcement learning
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Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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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
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Dorsa Sadigh, S Shankar Sastry, Sanjit A Seshia, and Anca Dragan · 2016
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Felix Berkenkamp, Matteo Turchetta, Angela Schoellig, and Andreas Krause · 2017
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Xiaowei Huang, Marta Kwiatkowska, Sen Wang, and Min Wu · 2017
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Reluplex: An efficient smt solver for verifying deep neural networks
Guy Katz, Clark Barrett, David L Dill, Kyle Julian, and Mykel J Kochenderfer · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Ai 2: Safety and robustness certification of neural networks with abstract interpretation
Timon Gehr, Matthew Mirman, Dana Drachsler-Cohen, Petar Tsankov, Swarat Chaudhuri, and Martin Vechev · 2018
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