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We propose a novel and flexible approach to meta-learning for learning-to-learn from only a few examples.
Neuronlike adaptive elements that can solve difficult learning control problems
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Meta-neural networks that learn by learning
D. K. Naik and R. J. Mammone · 1992
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Q-learning
C. Watkins and P. Dayan · 1992
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Advantage updating
L. C. B. III · 1993
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Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
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Shifting inductive bias with success-story algorithm, adaptive levin search, and incremental self-improvement
J. Schmidhuber, J. Zhao, and M. Wiering · 1997
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Learning to Learn
S. Thrun and L. Pratt, editors · 1998
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Policy gradient methods for reinforcement learning with function approximation
R. S. Sutton, D. McAllester, S. Singh, and Y. Mansour · 1999
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Model compression
C. Bucila, R. Caruana, and A. Niculescu-Mizil · 2006
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Zero-data learning of new tasks
H. Larochelle, D. Erhan, and Y. Bengio · 2008
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Visualizing high-dimensional data using t-sne
L. van der Maaten and G. E. Hinton · 2008
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Transfer learning for reinforcement learning domains: A survey
M. E. Taylor and P. Stone · 2009
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Learning parameterized skills
B. da Silva, G. Konidaris, and A. Barto · 2012
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A survey of actor-critic reinforcement learning: Standard and natural policy gradients
I. Grondman, L. Busoniu, G. Lopes, and R. Babuska · 2012
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Devise: A deep visual-semantic embedding model
A. Frome, G. Corrado, J. Shlens, S. Bengio, J. Dean, M. Ranzato, and T. Mikolov · 2013
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Data-efficient generalization of robot skills with contextual policy search
A. G. Kupcsik, M. P. Deisenroth, J. Peters, and G. Neumann · 2013
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Online multi-task learning for policy gradient methods
H. B. Ammar, E. Eaton, P. Ruvolo, and M. Taylor · 2014
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Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2014
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Deterministic policy gradient algorithms
D. Silver, G. Lever, N. Heess, T. Degris, D. Wierstra, and M. A. Riedmiller · 2014
RL 2 : Fast reinforcement learning via slow reinforcement learning
Y. Duan, J. Schulman, X. Chen, P. L. Bartlett, I. Sutskever, and P. Abbeel · 2016
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Continuous control with deep reinforcement learning
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra · 2016
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Asynchronous methods for deep reinforcement learning
V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu · 2016
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Actor-mimic: Deep multitask and transfer reinforcement learning
E. Parisotto, J. Ba, and R. Salakhutdinov · 2016
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Meta-learning with memory-augmented neural networks
A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap · 2016
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Siamese neural networks for one-shot image recognition
G. Koch, R. Zemel, and R. Salakhutdinov · 2015
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Human-level concept learning through probabilistic program induction
B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum · 2015
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Universal value function approximators
T. Schaul, D. Horgan, K. Gregor, and D. Silver · 2015
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Learning to learn by gradient descent by gradient descent
M. Andrychowicz, M. Denil, S. G. Colmenarejo, M. W. Hoffman, D. Pfau, T. Schaul, and N. de Freitas · 2016
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Learning feed-forward one-shot learners
L. Bertinetto, J. F. Henriques, J. Valmadre, P. H. S. Torr, and A. Vedaldi · 2016
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Openai gym, 2016
G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba · 2016
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J. Schulman, P. Moritz, S. Levine, M. I. Jordan, and P. Abbeel · 2016
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Matching networks for one shot learning
O. Vinyals, C. Blundell, T. Lillicrap, K. Kavukcuoglu, and D. Wierstra · 2016
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Counterfactual Multi-Agent Policy Gradients
J. Foerster, G. Farquhar, T. Afouras, N. Nardelli, and S. Whiteson · 2017
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Hypernetworks
D. Ha, A. Dai, and Q. Le · 2017
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Learning to optimize
K. Li and J. Malik · 2017
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Optimization as a model for few-shot learning
S. Ravi and H. Larochelle · 2017
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Tensor based knowledge transfer across skill categories for robot control
C. Zhao, T. Hospedales, F. Stulp, and O. Sigaud · 2017
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