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Recent unsupervised pre-training methods have shown to be effective on language and vision domains by learning useful representations for multiple downstream tasks.
Random projection in dimensionality reduction: applications to image and text data
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Visualizing data using t-sne
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Estimating or propagating gradients through stochastic neurons for conditional computation
Bengio, Y., Léonard, N., and Courville, A · 2013
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Efficient estimation of word representations in vector space
Mikolov, T., Chen, K., Corrado, G., and Dean, J · 2013
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Generative adversarial nets
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
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Modeling deep temporal dependencies with recurrent grammar cells
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Glove: Global vectors for word representation
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Video (language) modeling: a baseline for generative models of natural videos
Ranzato, M., Szlam, A., Bruna, J., Mathieu, M., Collobert, R., and Chopra, S · 2014
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Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al · 2015
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Action-conditional video prediction using deep networks in atari games
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High-dimensional continuous control using generalized advantage estimation
Schulman, J., Moritz, P., Levine, S., Jordan, M., and Abbeel, P · 2015
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Unsupervised learning of video representations using lstms
Srivastava, N., Mansimov, E., and Salakhudinov, R · 2015
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Tensorflow: A system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al · 2016
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Unifying count-based exploration and intrinsic motivation
Bellemare, M., Srinivasan, S., Ostrovski, G., Schaul, T., Saxton, D., and Munos, R · 2016
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Deep spatial autoencoders for visuomotor learning
Finn, C., Tan, X. Y., Duan, Y., Darrell, T., Levine, S., and Abbeel, P · 2016
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Vime: Variational information maximizing exploration
Houthooft, R., Chen, X., Duan, Y., Schulman, J., De Turck, F., and Abbeel, P · 2016
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End-to-end training of deep visuomotor policies
Levine, S., Finn, C., Darrell, T., and Abbeel, P · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Noroozi, M. and Favaro, P · 2016
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Generating videos with scene dynamics
Vondrick, C., Pirsiavash, H., and Torralba, A · 2016
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The” something something” video database for learning and evaluating visual common sense
Goyal, R., Ebrahimi Kahou, S., Michalski, V., Materzynska, J., Westphal, S., Kim, H., Haenel, V., Fruend, I., Yianilos, P., Mueller-Freitag, M., et al · 2017
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Darla: Improving zero-shot transfer in reinforcement learning
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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 · 2017
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Video pixel networks
Kalchbrenner, N., Oord, A., Simonyan, K., Danihelka, I., Vinyals, O., Graves, A., and Kavukcuoglu, K · 2017
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Deep predictive coding networks for video prediction and unsupervised learning
Lotter, W., Kreiman, G., and Cox, D · 2017
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Count-based exploration with neural density models
Ostrovski, G., Bellemare, M. G., Oord, A. v. d., and Munos, R · 2017
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Curiosity-driven exploration by self-supervised prediction
Pathak, D., Agrawal, P., Efros, A. A., and Darrell, T · 2017
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Parallel multiscale autoregressive density estimation
Reed, S., Oord, A., Kalchbrenner, N., Colmenarejo, S. G., Wang, Z., Chen, Y., Belov, D., and Freitas, N · 2017
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Mastering the game of go without human knowledge
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al · 2017
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# exploration: A study of count-based exploration for deep reinforcement learning
Tang, H., Houthooft, R., Foote, D., Stooke, A., Chen, O. X., Duan, Y., Schulman, J., DeTurck, F., and Abbeel, P · 2017
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Domain randomization for transferring deep neural networks from simulation to the real world
Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., and Abbeel, P · 2017
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Attention is all you need
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Aigner, S. and Körner, M · 2018
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Playing hard exploration games by watching youtube
Aytar, Y., Pfaff, T., Budden, D., Paine, T. L., Wang, Z., and de Freitas, N · 2018
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Stochastic variational video prediction
Babaeizadeh, M., Finn, C., Erhan, D., Campbell, R. H., and Levine, S · 2018
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Stochastic video generation with a learned prior
Denton, E. and Fergus, R · 2018
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Learning actionable representations from visual observations
Dwibedi, D., Tompson, J., Lynch, C., and Sermanet, P · 2018
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Unsupervised representation learning by predicting image rotations
Gidaris, S., Singh, P., and Komodakis, N · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Haarnoja, T., Zhou, A., Abbeel, P., and Levine, S · 2018
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Video prediction with appearance and motion conditions
Jang, Y., Kim, G., and Song, Y · 2018
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Stochastic adversarial video prediction
Lee, A. X., Zhang, R., Ebert, F., Abbeel, P., Finn, C., and Levine, S · 2018
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Imitation from observation: Learning to imitate behaviors from raw video via context translation
Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2020
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Rlbench: The robot learning benchmark & learning environment
James, S., Ma, Z., Arrojo, D. R., and Davison, A. J · 2020
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Learning to simulate dynamic environments with gamegan
Kim, S. W., Zhou, Y., Philion, J., Torralba, A., and Fidler, S · 2020
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Transformation-based adversarial video prediction on large-scale data
Luc, P., Clark, A., Dieleman, S., Casas, D. d. L., Doron, Y., Cassirer, A., and Simonyan, K · 2020
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Deep reinforcement and infomax learning
Mazoure, B., Combes, R. T. d., Doan, T., Bachman, P., and Hjelm, R. D · 2020
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Planning to explore via self-supervised world models
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Liu, Y., Gupta, A., Abbeel, P., and Levine, S · 2018
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Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O · 2018
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Sfv: Reinforcement learning of physical skills from videos
Peng, X. B., Kanazawa, A., Malik, J., Abbeel, P., and Levine, S · 2018
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Improving language understanding by generative pre-training
Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I · 2018
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Time-contrastive networks: Self-supervised learning from video
Sermanet, P., Lynch, C., Chebotar, Y., Hsu, J., Jang, E., Schaal, S., Levine, S., and Brain, G · 2018
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Reinforcement learning: An introduction
Sutton, R. S. and Barto, A. G · 2018
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Generative adversarial imitation from observation
Torabi, F., Warnell, G., and Stone, P · 2018
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Sekar, R., Rybkin, O., Daniilidis, K., Abbeel, P., Hafner, D., and Pathak, D · 2020
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Curl: Contrastive unsupervised representations for reinforcement learning
Srinivas, A., Laskin, M., and Abbeel, P · 2020
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dm_control: Software and tasks for continuous control
Tassa, Y., Tunyasuvunakool, S., Muldal, A., Doron, Y., Liu, S., Bohez, S., Merel, J., Erez, T., Lillicrap, T., and Heess, N · 2020
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Scaling autoregressive video models
Weissenborn, D., Tackstrom, O., and Uszkoreit, J · 2020
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Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
Yu, T., Quillen, D., He, Z., Julian, R., Hausman, K., Finn, C., and Levine, S · 2020
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A framework for efficient robotic manipulation
Zhan, A., Zhao, P., Pinto, L., Abbeel, P., and Laskin, M · 2020
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Deep reinforcement learning at the edge of the statistical precipice
Agarwal, R., Schwarzer, M., Castro, P. S., Courville, A. C., and Bellemare, M · 2021
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Fitvid: Overfitting in pixel-level video prediction
Babaeizadeh, M., Saffar, M. T., Nair, S., Levine, S., Finn, C., and Erhan, D · 2021
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Learning generalizable robotic reward functions from” in-the-wild” human videos
Chen, A. S., Nair, S., and Finn, C · 2021
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Mastering atari with discrete world models
Hafner, D., Lillicrap, T., Norouzi, M., and Ba, J · 2021
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Masked autoencoders are scalable vision learners
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., and Girshick, R · 2021
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Mt-opt: Continuous multi-task robotic reinforcement learning at scale
Kalashnikov, D., Varley, J., Chebotar, Y., Swanson, B., Jonschkowski, R., Finn, C., Levine, S., and Hausman, K · 2021
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Drivegan: Towards a controllable high-quality neural simulation
Kim, S. W., Philion, J., Torralba, A., and Fidler, S · 2021
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Urlb: Unsupervised reinforcement learning benchmark
Laskin, M., Yarats, D., Liu, H., Lee, K., Zhan, A., Lu, K., Cang, C., Pinto, L., and Abbeel, P · 2021
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Behavior from the void: Unsupervised active pre-training
Liu, H. and Abbeel, P · 2021
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Model-based reinforcement learning via latent-space collocation
Rybkin, O., Zhu, C., Nagabandi, A., Daniilidis, K., Mordatch, I., and Levine, S · 2021
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State entropy maximization with random encoders for efficient exploration
Seo, Y., Chen, L., Shin, J., Lee, H., Abbeel, P., and Lee, K · 2021
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Decoupling representation learning from reinforcement learning
Stooke, A., Lee, K., Abbeel, P., and Laskin, M · 2021
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Godiva: Generating open-domain videos from natural descriptions
Wu, C., Huang, L., Zhang, Q., Li, B., Ji, L., Yang, F., Sapiro, G., and Duan, N · 2021
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Videogpt: Video generation using vq-vae and transformers
Yan, W., Zhang, Y., Abbeel, P., and Srinivas, A · 2021
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Playvirtual: Augmenting cycle-consistent virtual trajectories for reinforcement learning
Yu, T., Lan, C., Zeng, W., Feng, M., Zhang, Z., and Chen, Z · 2021
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Learning invariant representations for reinforcement learning without reconstruction
Zhang, A., McAllister, R., Calandra, R., Gal, Y., and Levine, S · 2021
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Modeling purposeful adaptive behavior with the principle of maximum causal entropy, 2010
Ziebart, B. D · 2021
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Transdreamer: Reinforcement learning with transformer world models
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Masked visual pre-training for motor control
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