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We consider algorithms for learning reward functions from human preferences over pairs of trajectory segments, as used in reinforcement learning from human feedback (RLHF).
Q-learning
Christopher JCH Watkins and Peter Dayan · 1992
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Policy invariance under reward transformations: Theory and application to reward shaping
A.Y. Ng, D. Harada, and S. Russell · 1999
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Scikit-learn: Machine learning in Python
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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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Active preference-based learning of reward functions
Dorsa Sadigh, Anca D Dragan, Shankar Sastry, and Sanjit A Seshia · 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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Fine-tuning language models from human preferences
Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving · 2019
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Learning reward functions from diverse sources of human feedback: Optimally integrating demonstrations and preferences
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Training a helpful and harmless assistant with reinforcement learning from human feedback
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Chatgpt: Optimizing language models for dialogue
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Skill preferences: Learning to extract and execute robotic skills from human feedback
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Open problems and fundamental limitations of reinforcement learning from human feedback
Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, Jérémy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, et al · 2023
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Inverse preference learning: Preference-based rl without a reward function
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Models of human preference for learning reward functions
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Kimin Lee, Laura Smith, and Pieter Abbeel
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B-pref: Benchmarking preference-based reinforcement learning
Kimin Lee, Laura Smith, Anca Dragan, and Pieter Abbeel
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