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No real-world reward function is perfect.
Bandit Problems: Sequential Allocation of Experiments
Donald A Berry and Bert Fristedt · 1985
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Average Case Complexity under the Universal Distribution Equals Worst Case Complexity
Ming Li · 1992
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M.L. Puterman · 1994
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No Free Lunch Theorems for Optimization
David H Wolpert and William G Macready · 1997
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Planning and Acting in Partially Observable Stochastic Domains
Leslie Pack Kaelbling, Michael L. Littman, and Anthony R. Cassandra · 1998
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Richard S Sutton and Andrew G Barto · 1998
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Algorithms for Inverse Reinforcement Learning
Andrew Ng and Stuart Russell · 2000
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Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability
Marcus Hutter · 2005
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Owain Evans, Andreas Stuhlmuller, and Noah D Goodman · 2016
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Dylan Hadfield-Menell, Anca Dragan, Pieter Abbeel, and Stuart Russell · 2016
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Using Stories to Teach Human Values to Artificial Agents
Mark O Riedl and Brent Harrison · 2016
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Quantilizers: A Safer Alternative to Maximizers for Limited Optimization
Jessica Taylor · 2016
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Faulty Reward Functions in the Wild
Dario Amodei and Jack Clark · 2017
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Universal reinforcement learning algorithms: Survey and experiments
John Aslanides, Jan Leike, and Marcus Hutter · 2017
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Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané · 2016
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The Off-Switch Game
Dylan Hadfield-Menell, Anca Dragan, Pieter Abbeel, and Stuart Russell · 2017
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