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Learning good feature representations is important for deep reinforcement learning (RL).
Predictive information accelerates learning in RL
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Generative adversarial nets
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Human-level control through deep reinforcement learning
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Reinforcement learning with unsupervised auxiliary tasks
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Dueling network architectures for deep reinforcement learning
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Learning dense correspondence via 3d-guided cycle consistency
Zhou, T., Krahenbuhl, P., Aubry, M., Huang, Q., and Efros, A. A · 2016
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Learning to discover cross-domain relations with generative adversarial networks
Kim, T., Cha, M., Kim, H., Lee, J. K., and Kim, J · 2017
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Temporal ensembling for semi-supervised learning
Laine, S. and Aila, T · 2017
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Building machines that learn and think like people
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Loss is its own reward: Self-supervision for reinforcement learning
Shelhamer, E., Mahmoudieh, P., Argus, M., and Darrell, T · 2017
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Human learning in atari
Tsividis, P., Pouncy, T., Xu, J. L., Tenenbaum, J., and Gershman, S · 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
Zhu, J.-Y., Park, T., Isola, P., and Efros, A. A · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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Forward-backward reinforcement learning
Edwards, A. D., Downs, L., and Davidson, J. C · 2018
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Ha, D. and Schmidhuber, J · 2018
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Soft actor-critic algorithms and applications
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Rainbow: Combining improvements in deep reinforcement learning
Hessel, M., Modayil, J., Van Hasselt, H., Schaul, T., Ostrovski, G., Dabney, W., Horgan, D., Piot, B., Azar, M., and Silver, D · 2018
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Learning deep representations by mutual information estimation and maximization
Hjelm, R. D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Bachman, P., Trischler, A., and Bengio, Y · 2018
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Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation
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Learning to adapt in dynamic, real-world environments through meta-reinforcement learning
Nagabandi, A., Clavera, I., Liu, S., Fearing, R. S., Abbeel, P., Levine, S., and Finn, C · 2018
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Unsupervised learning of visual features by contrasting cluster assignments
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., and Joulin, A · 2020
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A simple framework for contrastive learning of visual representations
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The conditional entropy bottleneck
Fischer, I · 2020
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Bootstrap your own latent - a new approach to self-supervised learning
Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P., Buchatskaya, E., Doersch, C., Avila Pires, B., Guo, Z., Gheshlaghi Azar, M., Piot, B., kavukcuoglu, k., Munos, R., and Valko, M · 2020
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Bootstrap latent-predictive representations for multitask reinforcement learning
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Dream to control: Learning behaviors by latent imagination
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Oord, A. v. d., Li, Y., and Vinyals, O · 2018
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Deep co-training for semi-supervised image recognition
Qiao, S., Shen, W., Zhang, Z., Wang, B., and Yuille, A · 2018
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Tassa, Y., Doron, Y., Muldal, A., Erez, T., Li, Y., Casas, D. d. L., Budden, D., Abdolmaleki, A., Merel, J., Lefrancq, A., et al · 2018
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Unsupervised state representation learning in atari
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Dota 2 with large scale deep reinforcement learning
Berner, C., Brockman, G., Chan, B., Cheung, V., Dębiak, P., Dennison, C., Farhi, D., Fischer, Q., Hashme, S., Hesse, C., et al · 2019
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Temporal cycle-consistency learning
Dwibedi, D., Aytar, Y., Tompson, J., Sermanet, P., and Zisserman, A · 2019
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Recall traces: Backtracking models for efficient reinforcement learning
Goyal, A., Brakel, P., Fedus, W., Singhal, S., Lillicrap, T., Levine, S., Larochelle, H., and Bengio, Y · 2019
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Learning latent dynamics for planning from pixels
Hafner, D., Lillicrap, T., Fischer, I., Villegas, R., Ha, D., Lee, H., and Davidson, J · 2019
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Hafner, D., Lillicrap, T., Ba, J., and Norouzi, M · 2020
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Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2020
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Do recent advancements in model-based deep reinforcement learning really improve data efficiency?
Kielak, K · 2020
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Contrastive learning of structured world models
Kipf, T., van der Pol, E., and Welling, M · 2020
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Cycle-contrast for self-supervised video representation learning
Kong, Q., Wei, W., Deng, Z., Yoshinaga, T., and Murakami, T · 2020
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Deep reinforcement and infomax learning
Mazoure, B., Tachet des Combes, R., DOAN, T. L., Bachman, P., and Hjelm, R. D · 2020
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Trass: Time reversal as self-supervision
Nair, S., Babaeizadeh, M., Finn, C., Levine, S., and Kumar, V · 2020
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Decoupling representation learning from reinforcement learning
Stooke, A., Lee, K., Abbeel, P., and Laskin, M · 2020
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Learning cross-domain correspondence for control with dynamics cycle-consistency
Zhang, Q., Xiao, T., Efros, A. A., Pinto, L., and Wang, X · 2020
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Masked contrastive representation learning for reinforcement learning
Zhu, J., Xia, Y., Wu, L., Deng, J., Zhou, W., Qin, T., and Li, H · 2020
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Model based reinforcement learning for atari
Łukasz Kaiser, Babaeizadeh, M., Miłos, P., Osiński, B., Campbell, R. H., Czechowski, K., Erhan, D., Finn, C., Kozakowski, P., Levine, S., Mohiuddin, A., Sepassi, R., Tucker, G., and Michalewski, H · 2020
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Return-based contrastive representation learning for reinforcement learning
Liu, G., Zhang, C., Zhao, L., Qin, T., Zhu, J., Jian, L., Yu, N., and Liu, T.-Y · 2021
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Data-efficient reinforcement learning with self-predictive representations
Schwarzer, M., Anand, A., Goel, R., Hjelm, R. D., Courville, A., and Bachman, P · 2021
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Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
Yarats, D., Kostrikov, I., and Fergus, R · 2021
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Learning invariant representations for reinforcement learning without reconstruction
Zhang, A., McAllister, R. T., Calandra, R., Gal, Y., and Levine, S · 2021
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