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Imitation learning often needs a large demonstration set in order to handle the full range of situations that an agent might find itself in during deployment.
Reinforcement learning in markovian and non-markovian environments
Jürgen Schmidhuber · 1991
Earlier work this paper cites.
Efficient training of artificial neural networks for autonomous navigation
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Big self-supervised models are strong semi-supervised learners
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
David Ha and Jürgen Schmidhuber · 2018
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Dan Hendrycks, Norman Mu, Ekin Dogus Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan · 2019
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On variational bounds of mutual information
Ben Poole, Sherjil Ozair, Aaron van den Oord, Alexander A Alemi, and George Tucker · 2019
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Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, et al · 2020
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Decoupling representation learning from reinforcement learning
Adam Stooke, Kimin Lee, Pieter Abbeel, and Michael Laskin · 2020
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Ceb improves model robustness
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