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Preference-based reinforcement learning (RL) provides a framework to train agents using human preferences between two behaviors.
Rank analysis of incomplete block designs: I. the method of paired comparisons
Ralph Allan Bradley and Milton E Terry · 1952
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Peter Dayan · 1993
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Rewarding behaviors
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Structured solution methods for non-markovian decision processes
Fahiem Bacchus, Craig Boutilier, and Adam Grove · 1997
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Long short-term memory
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Daniel Kahneman · 2000
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Algorithms for inverse reinforcement learning
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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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Preference-based policy learning
Riad Akrour, Marc Schoenauer, and Michele Sebag · 2011
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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 S Sutton · 2011
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April: Active preference learning-based reinforcement learning
Riad Akrour, Marc Schoenauer, and Michèle Sebag · 2012
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Synthesis and stabilization of complex behaviors through online trajectory optimization
Yuval Tassa, Tom Erez, and Emanuel Todorov · 2012
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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A bayesian approach for policy learning from trajectory preference queries
Aaron Wilson, Alan Fern, and Prasad Tadepalli · 2012
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Active reward learning
C. Daniel, M. Viering, J. Metz, O. Kroemer, and J. Peters · 2014
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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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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Successor features for transfer in reinforcement learning
André Barreto, Will Dabney, Rémi Munos, Jonathan J Hunt, Tom Schaul, Hado P van Hasselt, and David Silver · 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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One-shot imitation learning
Yan Duan, Marcin Andrychowicz, Bradly Stadie, OpenAI Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, and Wojciech Zaremba · 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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Visual closed-loop control for pouring liquids
C. Schenck and D. Fox · 2017
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Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 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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Collective robot reinforcement learning with distributed asynchronous guided policy search
A. Yahya, A. Li, M. Kalakrishnan, Y. Chebotar, and S. Levine · 2017
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Compiling machine learning programs via high-level tracing
Roy Frostig, Matthew James Johnson, and Chris Leary · 2018
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Reward learning from human preferences and demonstrations in atari
Borja Ibarz, Jan Leike, Tobias Pohlen, Geoffrey Irving, Shane Legg, and Dario Amodei · 2018
Learning to summarize from human feedback
Nisan Stiennon, Long Ouyang, Jeff Wu, Daniel M Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul Christiano · 2020
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Atari-head: Atari human eye-tracking and demonstration dataset
Ruohan Zhang, Calen Walshe, Zhuode Liu, Lin Guan, Karl Muller, Jake Whritner, Luxin Zhang, Mary Hayhoe, and Dana Ballard · 2020
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robosuite: A modular simulation framework and benchmark for robot learning
Yuke Zhu, Josiah Wong, Ajay Mandlekar, and Roberto Martín-Martín · 2020
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Decision transformer: Reinforcement learning via sequence modeling
Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Misha Laskin, Pieter Abbeel, Aravind Srinivas, and Igor Mordatch · 2021
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Reinforcement learning as one big sequence modeling problem
Michael Janner, Qiyang Li, and Sergey Levine · 2021
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Improving language understanding by generative pre-training, 2018
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Rudder: Return decomposition for delayed rewards
Jose A Arjona-Medina, Michael Gillhofer, Michael Widrich, Thomas Unterthiner, Johannes Brandstetter, and Sepp Hochreiter · 2019
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Dota 2 with large scale deep reinforcement learning
Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemysław Dębiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Chris Hesse, et al · 2019
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Evolving rewards to automate reinforcement learning
Aleksandra Faust, Anthony Francis, and Dar Mehta · 2019
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From language to goals: Inverse reinforcement learning for vision-based instruction following
Justin Fu, Anoop Korattikara, Sergey Levine, and Sergio Guadarrama · 2019
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What matters in learning from offline human demonstrations for robot manipulation
Ajay Mandlekar, Danfei Xu, Josiah Wong, Soroush Nasiriany, Chen Wang, Rohun Kulkarni, Li Fei-Fei, Silvio Savarese, Yuke Zhu, and Roberto Martín-Martín · 2021
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Webgpt: Browser-assisted question-answering with human feedback
Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, et al · 2021
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Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
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Offline preference-based apprenticeship learning
Daniel Shin and Daniel S Brown · 2021
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Recursively summarizing books with human feedback
Jeff Wu, Long Ouyang, Daniel M Ziegler, Nissan Stiennon, Ryan Lowe, Jan Leike, and Paul Christiano · 2021
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Non-markovian reward modelling from trajectory labels via interpretable multiple instance learning
Joseph Early, Tom Bewley, Christine Evers, and Sarvapali Ramchurn · 2022
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Few-shot preference learning for human-in-the-loop RL
Donald Joseph Hejna III and Dorsa Sadigh · 2022
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Models of human preference for learning reward functions
W Bradley Knox, Stephane Hatgis-Kessell, Serena Booth, Scott Niekum, Peter Stone, and Alessandro Allievi · 2022
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Offline reinforcement learning with implicit q-learning
Ilya Kostrikov, Ashvin Nair, and Sergey Levine · 2022
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Reward uncertainty for exploration in preference-based reinforcement learning
Xinran Liang, Katherine Shu, Kimin Lee, and Pieter Abbeel · 2022
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Towards more generalizable one-shot visual imitation learning
Zhao Mandi, Fangchen Liu, Kimin Lee, and Pieter Abbeel · 2022
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Learning language-conditioned robot behavior from offline data and crowd-sourced annotation
Suraj Nair, Eric Mitchell, Kevin Chen, Brian Ichter, Silvio Savarese, and Chelsea Finn · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Surf: Semi-supervised reward learning with data augmentation for feedback-efficient preference-based reinforcement learning
Jongjin Park, Younggyo Seo, Jinwoo Shin, Honglak Lee, Pieter Abbeel, and Kimin Lee · 2022
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Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, et al · 2022
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Learning long-term reward redistribution via randomized return decomposition
Zhizhou Ren, Ruihan Guo, Yuan Zhou, and Jian Peng · 2022
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Symbol guided hindsight priors for reward learning from human preferences
Mudit Verma and Katherine Metcalf · 2022
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