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As reinforcement learning (RL) scales to solve increasingly complex tasks, interest continues to grow in the fields of AI safety and machine ethics.
Global Ethic: the Declaration of the Parliament of the World’s Religions
Hans Kng and Karl-Josef Kuschel · 1993
Earlier work this paper cites.
Introduction to reinforcement learning
Richard S Sutton, Andrew G Barto, et al · 1998
Earlier work this paper cites.
Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel and Andrew Y Ng · 2004
Earlier work this paper cites.
Intrinsically motivated reinforcement learning
Nuttapong Chentanez, Andrew G Barto, and Satinder P Singh · 2005
Earlier work this paper cites.
Cooperative multi-agent learning: The state of the art
Liviu Panait and Sean Luke · 2005
Earlier work this paper cites.
Moral emotions and moral behavior
June Price Tangney, Jeff Stuewig, and Debra J Mashek · 2007
Earlier work this paper cites.
Moral machines: Teaching robots right from wrong
Wendell Wallach and Colin Allen · 2008
Earlier work this paper cites.
Machine ethics
Michael Anderson and Susan Leigh Anderson · 2011
Earlier work this paper cites.
Towards an ethical robot: internal models, consequences and ethical action selection
Alan FT Winfield, Christian Blum, and Wenguo Liu · 2014
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
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Dylan Hadfield-Menell, Stuart J Russell, Pieter Abbeel, and Anca Dragan · 2016
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Jonathan Ho and Stefano Ermon · 2016
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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
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Tom Everitt, Gary Lea, and Marcus Hutter · 2018
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Measuring and avoiding side effects using relative reachability
Victoria Krakovna, Laurent Orseau, Miljan Martic, and Shane Legg · 2018
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Joel Lehman, Jeff Clune, Dusan Misevic, Christoph Adami, Lee Altenberg, Julie Beaulieu, Peter J Bentley, Samuel Bernard, Guillaume Beslon, David M Bryson, et al · 2018
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Scalable agent alignment via reward modeling: a research direction
Jan Leike, David Krueger, Tom Everitt, Miljan Martic, Vishal Maini, and Shane Legg · 2018
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Stuart Armstrong and Benjamin Levinstein · 2017
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Inverse reward design
Dylan Hadfield-Menell, Smitha Milli, Pieter Abbeel, Stuart J Russell, and Anca Dragan · 2017
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Third-person imitation learning
Bradly C Stadie, Pieter Abbeel, and Ilya Sutskever · 2017
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Roberta Raileanu, Emily Denton, Arthur Szlam, and Rob Fergus · 2018
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Trial without error: Towards safe reinforcement learning via human intervention
William Saunders, Girish Sastry, Andreas Stuhlmueller, and Owain Evans · 2018
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Abram Demski and Scott Garrabrant · 2019
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Conservative agency via attainable utility preservation
Alexander Matt Turner, Dylan Hadfield-Menell, and Prasad Tadepalli · 2019
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