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Self-supervised methods, wherein an agent learns representations solely by observing the results of its actions, become crucial in environments which do not provide a dense reward signal or have labels.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Mikolov, T., Chen, K., Corrado, G., and Dean, J · 2013
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Learning to see by moving
Agrawal, P., Carreira, J., and Malik, J · 2015
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Modeling video evolution for action recognition
Fernando, B., Gavves, E., Oramas, J. M., Ghodrati, A., and Tuytelaars, T · 2015
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Learning image representations tied to ego-motion
Jayaraman, D. and Grauman, K · 2015
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Learning state representations with robotic priors
Jonschkowski, R. and Brock, O · 2015
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Unsupervised learning of visual representations using videos
Wang, X. and Gupta, A · 2015
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Learning to poke by poking: Experiential learning of intuitive physics
Agrawal, P., Nair, A. V., Abbeel, P., Malik, J., and Levine, S · 2016
Earlier work this paper cites.
Unsupervised feature extraction by time-contrastive learning and nonlinear ica
Hyvarinen, A. and Morioka, H · 2016
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Reinforcement learning with unsupervised auxiliary tasks
Jaderberg, M., Mnih, V., Czarnecki, W. M., Schaul, T., Leibo, J. Z., Silver, D., and Kavukcuoglu, K · 2016
Earlier work this paper cites.
Learning to navigate in complex environments
Mirowski, P., Pascanu, R., Viola, F., Soyer, H., Ballard, A. J., Banino, A., Denil, M., Goroshin, R., Sifre, L., Kavukcuoglu, K., et al · 2016
Earlier work this paper cites.
Shuffle and learn: unsupervised learning using temporal order verification
Misra, I., Zitnick, C. L., and Hebert, M · 2016
Earlier work this paper cites.
Asynchronous methods for deep reinforcement learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., and Kavukcuoglu, K · 2016
Cited alongside, same era.
Loss is its own reward: Self-supervision for reinforcement learning
Shelhamer, E., Mahmoudieh, P., Argus, M., and Darrell, T · 2016
Cited alongside, same era.
Pygame learning environment
Tasfi, N · 2016
Cited alongside, same era.
Self-supervised video representation learning with odd-one-out networks
Fernando, B., Bilen, H., Gavves, E., and Gould, S · 2017
Cited alongside, same era.
Pves: Position-velocity encoders for unsupervised learning of structured state representations
Jonschkowski, R., Hafner, R., Scholz, J., and Riedmiller, M · 2017
Cited alongside, same era.
Nonlinear ica using auxiliary variables and generalized contrastive learning
Hyvarinen, A., Sasaki, H., and Turner, R. E · 2018
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Learning world models with self-supervised learning, 2018
LeCun, Y · 2018
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State representation learning for control: An overview
Lesort, T., Díaz-Rodríguez, N., Goudou, J.-F., and Filliat, D · 2018
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Gotta learn fast: A new benchmark for generalization in rl
Nichol, A., Pfau, V., Hesse, C., Klimov, O., and Schulman, J · 2018
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Deep contextualized word representations
Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., and Zettlemoyer, L · 2018
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Learning features by watching objects move
Pathak, D., Girshick, R., Dollár, P., Darrell, T., and Hariharan, B · 2017
Cited alongside, same era.
Time-contrastive networks: Self-supervised learning from video
Sermanet, P., Lynch, C., Chebotar, Y., Hsu, J., Jang, E., Schaal, S., and Levine, S · 2017
Cited alongside, same era.
Playing hard exploration games by watching youtube
Aytar, Y., Pfaff, T., Budden, D., Paine, T. L., Wang, Z., and de Freitas, N · 2018
Cited alongside, same era.
Large-scale study of curiosity-driven learning
Burda, Y., Edwards, H., Pathak, D., Storkey, A., Darrell, T., and Efros, A. A · 2018
Cited alongside, same era.
Contingency-aware exploration in reinforcement learning
Choi, J., Guo, Y., Moczulski, M., Oh, J., Wu, N., Norouzi, M., and Lee, H · 2018
Cited alongside, same era.
Senteval: An evaluation toolkit for universal sentence representations
Conneau, A. and Kiela, D · 2018
Cited alongside, same era.
Ha, D. and Schmidhuber, J · 2018
Cited alongside, same era.
S-rl toolbox: Environments, datasets and evaluation metrics for state representation learning
Raffin, A., Hill, A., Traoré, R., Lesort, T., Díaz-Rodríguez, N., and Filliat, D · 2018
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Learning general purpose distributed sentence representations via large scale multi-task learning
Subramanian, S., Trischler, A., Bengio, Y., and Pal, C. J · 2018
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Tracking emerges by colorizing videos
Vondrick, C., Shrivastava, A., Fathi, A., Guadarrama, S., and Murphy, K · 2018
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Learning and using the arrow of time
Wei, D., Lim, J. J., Zisserman, A., and Freeman, W. T · 2018
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Exploration by random distillation
Anonymous · 2019
Closest in time.
A theoretical analysis of contrastive unsupervised representation learning
Arora, S., Khandeparkar, H., Khodak, M., Plevrakis, O., and Saunshi, N · 2019
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