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The complexity of designing reward functions has been a major obstacle to the wide application of deep reinforcement learning (RL) techniques.
Rank analysis of incomplete block designs: I. the method of paired comparisons
Ralph Allan Bradley and Milton E Terry · 1952
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A bayesian approach for policy learning from trajectory preference queries
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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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Mastering the game of go with deep neural networks and tree search
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Earlier work this paper cites.
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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Deepstack: Expert-level artificial intelligence in heads-up no-limit poker
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Batch active preference-based learning of reward functions
Erdem Biyik and Dorsa Sadigh · 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
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Scalable agent alignment via reward modeling: a research direction
Jan Leike, David Krueger, Tom Everitt, Miljan Martic, Vishal Maini, and Shane Legg · 2018
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A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al · 2018
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Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdolmaleki, Josh Merel, Andrew Lefrancq, et al · 2018
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Grandmaster level in starcraft ii using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al · 2019
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Learning to summarize with human feedback
Pebble: Feedback-efficient interactive reinforcement learning via relabeling experience and unsupervised pre-training
Kimin Lee, Laura M Smith, and Pieter Abbeel · 2021
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Crowdplay: Crowdsourcing human demonstrations for offline learning
Matthias Gerstgrasser, Rakshit Trivedi, and David C. Parkes · 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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Reward uncertainty for exploration in preference-based reinforcement learning
Xinran Liang, Katherine Shu, Kimin Lee, and Pieter Abbeel · 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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dm_control: Software and tasks for continuous control
Saran Tunyasuvunakool, Alistair Muldal, Yotam Doron, Siqi Liu, Steven Bohez, Josh Merel, Tom Erez, Timothy Lillicrap, Nicolas Heess, and Yuval Tassa · 2020
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Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, and Sergey Levine · 2020
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Weak human preference supervision for deep reinforcement learning
Zehong Cao, KaiChiu Wong, and Chin-Teng Lin · 2021
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B-pref: Benchmarking preference-based reinforcement learning
Kimin Lee, Laura Smith, Anca Dragan, and Pieter Abbeel · 2021
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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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Outracing champion gran turismo drivers with deep reinforcement learning
Peter R Wurman, Samuel Barrett, Kenta Kawamoto, James MacGlashan, Kaushik Subramanian, Thomas J Walsh, Roberto Capobianco, Alisa Devlic, Franziska Eckert, Florian Fuchs, et al · 2022
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Prefrec: Recommender systems with human preferences for reinforcing long-term user engagement
Wanqi Xue, Qingpeng Cai, Zhenghai Xue, Shuo Sun, Shuchang Liu, Dong Zheng, Peng Jiang, Kun Gai, and Bo An · 2023
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Beyond one-preference-for-all: Multi-objective direct preference optimization, 2024
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