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Meta-learning algorithms use past experience to learn to quickly solve new tasks.
Efficient exploration via state marginal matching
Lisa Lee, Benjamin Eysenbach, Emilio Parisotto, Eric P. Xing, Sergey Levine, and Ruslan Salakhutdinov · 1906
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Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-… hook
Jürgen Schmidhuber · 1987
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On the optimization of a synaptic learning rule
Samy Bengio, Yoshua Bengio, Jocelyn Cloutier, and Jan Gecsei · 1992
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Meta-neural networks that learn by learning
Devang K Naik and RJ Mammone · 1992
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No free lunch theorems for search
David H Wolpert, William G Macready, et al · 1995
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A comparison of direct and model-based reinforcement learning
Christopher G Atkeson and Juan Carlos Santamaria · 1997
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Learning to learn
Sebastian Thrun and Lorien Pratt · 1998
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Learning to learn using gradient descent
Sepp Hochreiter, A Steven Younger, and Peter R Conwell · 2001
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Complexity theory and the no free lunch theorem, 2005
Darrell Whitley and Jean Paul Watson · 2005
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Driven by compression progress: A simple principle explains essential aspects of subjective beauty, novelty, surprise, interestingness, attention, curiosity, creativity, art, science, music, jokes
Jürgen Schmidhuber · 2009
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Pilco: A model-based and data-efficient approach to policy search
Marc Deisenroth and Carl E Rasmussen · 2011
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Data-dependent initializations of convolutional neural networks
Philipp Krähenbühl, Carl Doersch, Jeff Donahue, and Trevor Darrell · 2015
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Universal value function approximators
Tom Schaul, Daniel Horgan, Karol Gregor, and David Silver · 2015
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Incentivizing exploration in reinforcement learning with deep predictive models
Bradly C Stadie, Sergey Levine, and Pieter Abbeel · 2015
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Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, and Nando de Freitas · 2016
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Unifying count-based exploration and intrinsic motivation
Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, and Rémi Munos · 2016
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Karol Gregor, Danilo Jimenez Rezende, and Daan Wierstra · 2016
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VIME: variational information maximizing exploration
Rein Houthooft, Xi Chen, Yan Duan, John Schulman, Filip De Turck, and Pieter Abbeel · 2016
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Deep exploration via bootstrapped DQN
Ian Osband, Charles Blundell, Alexander Pritzel, and Benjamin Van Roy · 2016
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Meta-learning with memory-augmented neural networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap · 2016
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Learning to reinforcement learn
Jane X Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z Leibo, Remi Munos, Charles Blundell, Dharshan Kumaran, and Matt Botvinick · 2016
Cited alongside, same era.
Continuous adaptation via meta-learning in nonstationary and competitive environments
Maruan Al-Shedivat, Trapit Bansal, Yuri Burda, Ilya Sutskever, Igor Mordatch, and Pieter Abbeel · 2017
Cited alongside, same era.
Hindsight experience replay
Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, OpenAI Pieter Abbeel, and Wojciech Zaremba · 2017
Meta-learning and universality: Deep representations and gradient descent can approximate any learning algorithm
Chelsea Finn and Sergey Levine · 2018
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Meta-reinforcement learning of structured exploration strategies
Abhishek Gupta, Russell Mendonca, YuXuan Liu, Pieter Abbeel, and Sergey Levine · 2018
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Rein Houthooft, Richard Y Chen, Phillip Isola, Bradly C Stadie, Filip Wolski, Jonathan Ho, and Pieter Abbeel · 2018
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Unsupervised learning via meta-learning
Kyle Hsu, Sergey Levine, and Chelsea Finn · 2018
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Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning
Anusha Nagabandi, Gregory Kahn, Ronald S Fearing, and Sergey Levine · 2018
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Cited alongside, same era.
Deep visual foresight for planning robot motion
Chelsea Finn and Sergey Levine · 2017
Cited alongside, same era.
Automatic goal generation for reinforcement learning agents
David Held, Xinyang Geng, Carlos Florensa, and Pieter Abbeel · 2017
Cited alongside, same era.
A simple neural attentive meta-learner
Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel · 2017
Cited alongside, same era.
Meta networks
Tsendsuren Munkhdalai and Hong Yu · 2017
Cited alongside, same era.
Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, and Trevor Darrell · 2017
Cited alongside, same era.
Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
Cited alongside, same era.
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Temporal difference models: Model-free deep rl for model-based control
Vitchyr Pong, Shixiang Gu, Murtaza Dalal, and Sergey Levine · 2018
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Aravind Srinivas, Allan Jabri, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 2018
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Some considerations on learning to explore via meta-reinforcement learning
Bradly C. Stadie, Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, and Ilya Sutskever · 2018
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Unsupervised control through non-parametric discriminative rewards
David Warde-Farley, Tom Van de Wiele, Tejas Kulkarni, Catalin Ionescu, Steven Hansen, and Volodymyr Mnih · 2018
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Antreas Antoniou and Amos Storkey · 2019
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Unsupervised curricula for visual meta-reinforcement learning
Allan Jabri, Kyle Hsu, Abhishek Gupta, Ben Eysenbach, Sergey Levine, and Chelsea Finn · 2019
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Unsupervised few-shot learning via self-supervised training
Zilong Ji, Xiaolong Zou, Tiejun Huang, and Si Wu · 2019
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Learning to transfer: Unsupervised meta domain translation
Jianxin Lin, Yijun Wang, Yingce Xia, Tianyu He, and Zhibo Chen · 2019
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Russell Mendonca, Abhishek Gupta, Rosen Kralev, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 2019
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Efficient off-policy meta-reinforcement learning via probabilistic context variables
Kate Rakelly, Aurick Zhou, Deirdre Quillen, Chelsea Finn, and Sergey Levine · 2019
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Promp: Proximal meta-policy search
Jonas Rothfuss, Dennis Lee, Ignasi Clavera, Tamim Asfour, and Pieter Abbeel · 2019
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Dynamics-aware unsupervised discovery of skills
Archit Sharma, Shixiang Gu, Sergey Levine, Vikash Kumar, and Karol Hausman · 2019
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