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Reinforcement learning (RL) agents optimize only the features specified in a reward function and are indifferent to anything left out inadvertently.
Some philosophical problems from the standpoint of artificial intelligence
John McCarthy and Patrick J Hayes · 1981
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Learning to achieve goals
Leslie Pack Kaelbling · 1993
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Bayesian inverse reinforcement learning
Deepak Ramachandran and Eyal Amir · 2007
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A Survey of Preference-Based Reinforcement Learning Methods
Christian Wirth, Riad Akrour, Gerhard Neumann, and Johannes Fürnkranz · 2008
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Interactively shaping agents via human reinforcement: The tamer framework
W Bradley Knox and Peter Stone · 2009
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Modeling interaction via the principle of maximum causal entropy
Brian D Ziebart, J Andrew Bagnell, and Anind K Dey · 2010
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Active reward learning
Christian Daniel, Malte Viering, Jan Metz, Oliver Kroemer, and Jan Peters · 2014
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Universal value function approximators
Tom Schaul, Daniel Horgan, Karol Gregor, and David Silver · 2015
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Concrete problems in ai safety
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané · 2016
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Guided cost learning: Deep inverse optimal control via policy optimization
Chelsea Finn, Sergey Levine, and Pieter Abbeel · 2016
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Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
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Repeated inverse reinforcement learning
Kareem Amin, Nan Jiang, and Satinder Singh · 2017
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Static analysis of functional programs with an application to the frame problem in deductive verification
Oana Fabiana Andreescu · 2017
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Hindsight experience replay
Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, Pieter Abbeel, and Wojciech Zaremba · 2017
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Low impact artificial intelligences
Stuart Armstrong and Benjamin Levinstein · 2017
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Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
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Learning robust rewards with adversarial inverse reinforcement learning
Variational option discovery algorithms
Joshua Achiam, Harrison Edwards, Dario Amodei, and Pieter Abbeel · 2018
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Learning to understand goal specifications by modelling reward
Dzmitry Bahdanau, Felix Hill, Jan Leike, Edward Hughes, Pushmeet Kohli, and Edward Grefenstette · 2018
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Large-scale study of curiosity-driven learning
Yuri Burda, Harri Edwards, Deepak Pathak, Amos Storkey, Trevor Darrell, and Alexei A Efros · 2018
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Imitating latent policies from observation
Ashley D Edwards, Himanshu Sahni, Yannick Schroeker, and Charles L Isbell · 2018
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Diversity is all you need: Learning skills without a reward function
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Justin Fu, Katie Luo, and Sergey Levine · 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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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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Active preference-based learning of reward functions
Dorsa Sadigh, Anca Dragan, Shankar Sastry, and Sanjit A Seshia · 2017
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Deep tamer: Interactive agent shaping in high-dimensional state spaces
Garrett Warnell, Nicholas Waytowich, Vernon Lawhern, and Peter Stone · 2017
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Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine · 2018
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Inverse reinforcement learning from summary data
Antti Kangasrääsiö and Samuel Kaski · 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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Visual reinforcement learning with imagined goals
Ashvin Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, and Sergey Levine · 2018
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Towards a new impact measure, 2018
Alex Turner · 2018
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One-shot imitation from observing humans via domain-adaptive meta-learning
Tianhe Yu, Chelsea Finn, Annie Xie, Sudeep Dasari, Tianhao Zhang, Pieter Abbeel, and Sergey Levine · 2018
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