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We build deep RL agents that execute declarative programs expressed in formal language.
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I. Bratko · 2001
Earlier work this paper cites.
Adaptive weighing of context models for lossless data compression
M. V. Mahoney · 2005
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A machine learning perspective on predictive coding with PAQ8
B. Knoll and N. de Freitas · 2012
Earlier work this paper cites.
Semantic compositionality through recursive matrix-vector spaces
R. Socher, B. Huval, C. D. Manning, and A. Y. Ng · 2012
Earlier work this paper cites.
Mujoco: A physics engine for model-based control
E. Todorov, T. Erez, and Y. Tassa · 2012
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean · 2013
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
R. Socher, A. Perelygin, J. Y. Wu, J. Chuang, C. D. Manning, A. Y. Ng, C. Potts, et al · 2013
Earlier work this paper cites.
Deterministic policy gradient algorithms
D. Silver, G. Lever, N. Heess, T. Degris, D. Wierstra, and M. Riedmiller · 2014
Earlier work this paper cites.
W. Zaremba and I. Sutskever · 2014
Earlier work this paper cites.
Learning to transduce with unbounded memory
E. Grefenstette, K. M. Hermann, M. Suleyman, and P. Blunsom · 2015
Earlier work this paper cites.
Neural GPUs learn algorithms
Ł. Kaiser and I. Sutskever · 2015
Earlier work this paper cites.
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 · 2015
Earlier work this paper cites.
Human-level control through deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al · 2015
Earlier work this paper cites.
Learning to compose neural networks for question answering
J. Andreas, M. Rohrbach, T. Darrell, and D. Klein · 2016
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J. Andreas, M. Rohrbach, T. Darrell, and D. Klein · 2016
Cited alongside, same era.
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P. W. Battaglia, R. Pascanu, M. Lai, D. Rezende, and K. Kavukcuoglu · 2016
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Programming with a differentiable forth interpreter
M. Bošnjak, T. Rocktäschel, J. Naradowsky, and S. Riedel · 2016
Cited alongside, same era.
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C. Devin, A. Gupta, T. Darrell, P. Abbeel, and S. Levine · 2016
Cited alongside, same era.
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A. Graves, G. Wayne, M. Reynolds, T. Harley, I. Danihelka, A. Grabska-Barwińska, S. G. Colmenarejo, E. Grefenstette, T. Ramalho, J. Agapiou, et al · 2016
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M. B. Chang, T. Ullman, A. Torralba, and J. B. Tenenbaum · 2017
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J. Devlin, J. Uesato, S. Bhupatiraju, R. Singh, A. Mohamed, and P. Kohli · 2017
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Y. Duan, M. Andrychowicz, B. C. Stadie, J. Ho, J. Schneider, I. Sutskever, P. Abbeel, and W. Zaremba · 2017
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J. Johnson, B. Hariharan, L. van der Maaten, J. Hoffman, L. Fei-Fei, C. L. Zitnick, and R. Girshick · 2017
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