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Despite the remarkable success of Deep RL in learning control policies from raw pixels, the resulting models do not generalize.
Reducing the dimensionality of data with neural networks
Hinton, G. E. and Salakhutdinov, R. R · 2006
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Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y · 2010
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No-regret reductions for imitation learning and structured prediction
Ross, S., Gordon, G. J., and Bagnell, J. A · 2010
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Deep learning of representations for unsupervised and transfer learning
Bengio, Y · 2012
Earlier work this paper cites.
Unsupervised and transfer learning challenge: a deep learning approach
Dauphin, G. M. Y., Glorot, X., Rifai, S., Bengio, Y., Goodfellow, I., Lavoie, E., Muller, X., Desjardins, G., Warde-Farley, D., Vincent, P., Courville, A., and Bergstra, J · 2012
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Playing atari with deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmillera, M. A · 2013
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., and Lipson, H · 2014
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Schaul, T., Quan, J., Antonoglou, I., and Silver, D · 2015
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Unsupervised pixel-level domain adaptation with generative adversarial networks
Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., and Krishnan, D · 2016
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Generative adversarial imitation learning
Ho, J. and Ermon, S · 2016
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Image-to-image translation with conditional adversarial networks
Isola, P., Zhu, J., Zhou, T., and Efros, A. A · 2016
Cited alongside, same era.
Coupled generative adversarial networks
Liu, M. and Tuzel, O · 2016
Cited alongside, same era.
Asynchronous methods for deep reinforcement learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T. P., Harley, T., Silver, D., and Kavukcuoglu, K · 2016
Cited alongside, same era.
Rusu, A. A., Rabinowitz, N. C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., and Hadsell, R · 2016
Cited alongside, same era.
Using simulation and domain adaptation to improve efficiency of deep robotic grasping
Learning to discover cross-domain relations with generative adversarial networks
Kim, T., Cha, M., Kim, H., Lee, J. K., and Kim, J · 2017
Later among the works it cites.
Unsupervised image-to-image translation networks
Liu, M., Breuel, T., and Kautz, J · 2017
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Zero-shot task generalization with multi-task deep reinforcement learning
Oh, J., Singh, S. P., Lee, H., and Kohli, P · 2017
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Dualgan: Unsupervised dual learning for image-to-image translation
Yi, Z., Zhang, H., Tan, P., and Gong, M · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
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Bousmalis, K., Irpan, A., Wohlhart, P., Bai, Y., Kelcey, M., Kalakrishnan, M., Downs, L., Ibarz, J., Pastor, P., Konolige, K., Levine, S., and Vanhoucke, V · 2017
Cited alongside, same era.
One-shot reinforcement learning for robot navigation with interactive replay
Bruce, J., Sünderhauf, N., Mirowski, P., Hadsell, R., and Milford, M · 2017
Cited alongside, same era.
Pathnet: Evolution channels gradient descent in super neural networks
Fernando, C., Banarse, D., Blundell, C., Zwols, Y., Ha, D., Rusu, A. A., Pritzel, A., and Wierstra, D · 2017
Cited alongside, same era.
Learning from demonstrations for real world reinforcement learning
Hester, T., Vecerik, M., Pietquin, O., Lanctot, M., Schaul, T., Piot, B., Sendonaris, A., Dulac-Arnold, G., Osband, I., Agapiou, J., Leibo, J. Z., and Gruslys, A · 2017
Cited alongside, same era.
Darla: Improving zero-shot transfer in reinforcement learning
Higgins, I., Pal, A., Rusu, A. A., Matthey, L., Burgess, C., Pritzel, A., Botvinick, M. M., Blundell, C., and Lerchner, A · 2017
Cited alongside, same era.
Cycada: Cycle-consistent adversarial domain adaptation
Hoffman, J., Tzeng, E., Park, T., Zhu, J., Isola, P., Saenko, K., Efros, A. A., and Darrell, T · 2017
Cited alongside, same era.
Schema networks: Zero-shot transfer with a generative causal model of intuitive physics
Kansky, K., Silver, T., Mély, D. A., Eldawy, M., Lázaro-Gredilla, M., Lou, X., Dorfman, N., Sidor, S., Phoenix, D. S., and George, D · 2017
Cited alongside, same era.
Pytorch implementations of asynchronous advantage actor critic
Kostrikov, I
Cited in the paper.
Zhu, J., Park, T., Isola, P., and Efros, A. A · 2017
Later among the works it cites.
IMPALA: scalable distributed deep-rl with importance weighted actor-learner architectures
Espeholt, L., Soyer, H., Munos, R., Simonyan, K., Mnih, V., Ward, T., Doron, Y., Firoiu, V., Harley, T., Dunning, I., Legg, S., and Kavukcuoglu, K · 2018
Closest in time.
Reinforcement learning from imperfect demonstrations
Gao, Y., Xu, H., Lin, J., Yu, F., Levine, S., and Darrell, T · 2018
Closest in time.
Oh, J., Guo, Y., Singh, S., and Lee, H · 2018
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Multitask reinforcement learning for zero-shot generalization with subtask dependencies
Sohn, S., Oh, J., and Lee, H · 2018
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Behavioral cloning from observation
Torabi, F., Warnell, G., and Stone, P · 2018
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A study on overfitting in deep reinforcement learning
Zhang, C., Vinyals, O., Munos, R., and Bengio, S · 2018
Closest in time.