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In offline reinforcement learning (RL) agents are trained using a logged dataset.
Probability of error of some adaptive pattern-recognition machines
H. Scudder · 1965
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ALVINN: An autonomous land vehicle in a neural network
Dean A Pomerleau · 1989
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Combining labeled and unlabeled data with co-training
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Algorithms for inverse reinforcement learning
Andrew Y Ng and Stuart Russell · 2000
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Apprenticeship learning via inverse reinforcement learning
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David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin Riedmiller · 2014
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Jonathan Ho and Stefano Ermon · 2016
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Nir Baram, Oron Anschel, Itai Caspi, and Shie Mannor · 2017
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Marc G Bellemare, Will Dabney, and Rémi Munos · 2017
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Positive-unlabeled learning with non-negative risk estimator
Ryuichi Kiryo, Gang Niu, Marthinus C Du Plessis, and Masashi Sugiyama · 2017
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Yunzhu Li, Jiaming Song, and Stefano Ermon · 2017
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Off-policy deep reinforcement learning without exploration
Scott Fujimoto, David Meger, and Doina Precup · 2019
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Xue Bin Peng, Aviral Kumar, Grace Zhang, and Sergey Levine · 2019
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End-to-end robotic reinforcement learning without reward engineering
Avi Singh, Larry Yang, Kristian Hartikainen, Chelsea Finn, and Sergey Levine · 2019
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Positive-unlabeled reward learning
Danfei Xu and Misha Denil · 2019
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Task-relevant adversarial imitation learning
Konrad Zolna, Scott Reed, Alexander Novikov, Sergio Gomez Colmenarej, David Budden, Serkan Cabi, Misha Denil, Nando de Freitas, and Ziyu Wang · 2019
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