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Many real world learning tasks involve complex or hard-to-specify objectives, and using an easier-to-specify proxy can lead to poor performance or misaligned behavior.
Algorithms for inverse reinforcement learning
Andrew Y Ng and Stuart Russell · 2000
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Algorithms for inverse reinforcement learning
Andrew Y Ng, Stuart J Russell, et al · 2000
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Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel and Andrew Y Ng · 2004
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Interactively shaping agents via human reinforcement: The TAMER framework
W Bradley Knox and Peter Stone · 2009
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Online human training of a myoelectric prosthesis controller via actor-critic reinforcement learning
Patrick M Pilarski, Michael R Dawson, Thomas Degris, Farbod Fahimi, Jason P Carey, and Richard Sutton · 2011
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Superintelligence: Paths, Dangers, Strategies
Nick Bostrom · 2014
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Łukasz Kaiser and Ilya Sutskever · 2015
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Neural programmer: Inducing latent programs with gradient descent
Arvind Neelakantan, Quoc V Le, and Ilya Sutskever · 2015
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Cooperative inverse reinforcement learning
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Deep reinforcement learning from human preferences
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Interactive learning from policy-dependent human feedback
James MacGlashan, Mark K Ho, Robert Loftin, Bei Peng, David Roberts, Matthew E Taylor, and Michael L Littman · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Dylan Hadfield-Menell, Stuart J Russell, Pieter Abbeel, and Anca Dragan · 2016
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Alex Nowak and Joan Bruna · 2016
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Mastering chess and shogi by self-play with a general reinforcement learning algorithm
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Geoffrey Irving, Paul Christiano, and Dario Amodei · 2018
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