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The recent history of machine learning research has taught us that machine learning methods can be most effective when they are provided with very large, high-capacity models, and trained on very large and diverse datasets.
From socrates to expert systems: The limits of calculative rationality
H. L. Dreyfus and S. E. Dreyfus · 1986
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Learning to achieve goals
L. P. Kaelbling · 1993
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Improving generalization for temporal difference learning: The successor representation
P. Dayan · 1993
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The playground experiment: Task-independent development of a curious robot
P.-Y. Oudeyer, F. Kaplan, V. V. Hafner, and A. Whyte · 2005
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Abandoning objectives: Evolution through the search for novelty alone
J. Lehman and K. O. Stanley · 2011
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
G. Hinton, L. Deng, D. Yu, G. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. Sainath, et al · 2012
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Intrinsic motivation and reinforcement learning
A. G. Barto · 2013
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Sequence to sequence learning with neural networks
I. Sutskever, O. Vinyals, and Q. Le · 2014
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Empowerment–an introduction
C. Salge, C. Glackin, and D. Polani · 2014
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Unifying count-based exploration and intrinsic motivation
M. G. Bellemare, S. Srinivasan, G. Ostrovski, T. Schaul, D. Saxton, and R. Munos · 2016
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K. Gregor, D. J. Rezende, and D. Wierstra · 2016
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Building machines that learn and think like people
B. M. Lake, T. D. Ullman, J. B. Tenenbaum, and S. J. Gershman · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M. Chang, K. Lee, and K. Toutanova · 2018
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Deep contextualized word representations
M. E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, and L. Zettlemoyer · 2018
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Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation
D. Kalashnikov, A. Irpan, P. Pastor, J. Ibarz, A. Herzog, E. Jang, D. Quillen, E. Holly, M. Kalakrishnan, V. Vanhoucke, and S. Levine · 2018
Cited alongside, same era.
Unsupervised control through non-parametric discriminative rewards
D. Warde-Farley, T. Van de Wiele, T. Kulkarni, C. Ionescu, S. Hansen, and V. Mnih · 2018
Offline reinforcement learning: Tutorial, review, and perspectives on open problems
S. Levine, A. Kumar, G. Tucker, and J. Fu · 2020
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Accelerating online reinforcement learning with offline datasets
A. Nair, M. Dalal, A. Gupta, and S. Levine · 2020
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C-learning: Learning to achieve goals via recursive classification
B. Eysenbach, R. Salakhutdinov, and S. Levine · 2020
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γ \gamma -models: Generative temporal difference learning for infinite-horizon prediction
M. Janner, I. Mordatch, and S. Levine · 2020
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Cited alongside, same era.
Temporal difference models: Model-free deep rl for model-based control
V. Pong, S. Gu, M. Dalal, and S. Levine · 2018
Cited alongside, same era.
Diversity is all you need: Learning skills without a reward function
B. Eysenbach, A. Gupta, J. Ibarz, and S. Levine · 2018
Cited alongside, same era.
The bitter lesson, 2019
R. Sutton · 2019
Cited alongside, same era.
Skew-fit: State-covering self-supervised reinforcement learning
V. H. Pong, M. Dalal, S. Lin, A. Nair, S. Bahl, and S. Levine · 2019
Cited alongside, same era.
Dynamics-aware unsupervised discovery of skills
A. Sharma, S. Gu, S. Levine, V. Kumar, and K. Hausman · 2019
Cited alongside, same era.
Stabilizing off-policy q-learning via bootstrapping error reduction
A. Kumar, J. Fu, G. Tucker, and S. Levine · 2019
Cited alongside, same era.
A simple framework for contrastive learning of visual representations
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton · 2020
Cited alongside, same era.
A. Kumar, A. Zhou, G. Tucker, and S. Levine · 2020
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Model-based visual planning with self-supervised functional distances
S. Tian, S. Nair, F. Ebert, S. Dasari, B. Eysenbach, C. Finn, and S. Levine · 2020
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Toward causal representation learning
B. Schölkopf, F. Locatello, S. Bauer, N. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio · 2021
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Self-supervised learning: The dark matter of intelligence, 2021
Y. LeCun and I. Misra · 2021
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Outcome-driven reinforcement learning via variational inference
T. G. Rudner, V. H. Pong, R. McAllister, Y. Gal, and S. Levine · 2021
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Actionable models: Unsupervised offline reinforcement learning of robotic skills
Y. Chebotar, K. Hausman, Y. Lu, T. Xiao, D. Kalashnikov, J. Varley, A. Irpan, B. Eysenbach, R. Julian, C. Finn, and S. Levine · 2021
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RECON: Rapid exploration for open-world navigation with latent goal models
D. Shah, B. Eysenbach, N. Rhinehart, and S. Levine · 2021
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