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High-dimensional observations are a major challenge in the application of model-based reinforcement learning (MBRL) to real-world environments.
A simple framework for contrastive learning of visual representations
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Human-level control through deep reinforcement learning
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Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, D · 2016
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The kinetics human action video dataset
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Building machines that learn and think like people
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Playing fps games with deep reinforcement learning
Lample, G. and Chaplot, D. S · 2017
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Robust locally-linear controllable embedding
Banijamali, E., Shu, R., Ghavamzadeh, M., Bui, H., and Ghodsi, A · 2018
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Recurrent world models facilitate policy evolution
Ha, D. and Schmidhuber, J · 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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Noise contrastive estimation and negative sampling for conditional models: Consistency and statistical efficiency
Ma, Z. and Collins, M · 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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Reinforcement learning: An introduction
Sutton, R. S. and Barto, A. G · 2018
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Control-aware representations for model-based reinforcement learning
Cui, B., Chow, Y., and Ghavamzadeh, M · 2020
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Mutual information maximization for robust plannable representations
Ding, Y., Clavera, I., and Abbeel, P · 2020
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Dream to control: Learning behaviors by latent imagination
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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Model based reinforcement learning for atari
Kaiser, L., Babaeizadeh, M., Milos, P., Osinski, 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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Learning representations by maximizing mutual information across views
Bachman, P., Hjelm, R. D., and Buchwalter, W · 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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Data-efficient image recognition with contrastive predictive coding
Hénaff, O. J., Srinivas, A., De Fauw, J., Razavi, A., Doersch, C., Eslami, S., and Oord, A. v. d · 2019
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On variational bounds of mutual information
Poole, B., Ozair, S., Van Den Oord, A., Alemi, A., and Tucker, G · 2019
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Tian, Y., Krishnan, D., and Isola, P · 2019
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SOLAR: Deep structured representations for model-based reinforcement learning
Zhang, M., Vikram, S., Smith, L., Abbeel, P., Johnson, M., and Levine, S · 2019
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Prediction, consistency, curvature: Representation learning for locally-linear control
Levine, N., Chow, Y., Shu, R., Li, A., Ghavamzadeh, M., and Bui, H · 2020
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Contrastive variational model-based reinforcement learning for complex observations
Ma, X., Chen, S., Hsu, D., and Lee, W. S · 2020
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Predictive coding for locally-linear control
Shu, R., Nguyen, T., Chow, Y., Pham, T., Than, K., Ghavamzadeh, M., Ermon, S., and Bui, H. H · 2020
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Curl: Contrastive unsupervised representations for reinforcement learning
Srinivas, A., Laskin, M., and Abbeel, P · 2020
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A theory of usable information under computational constraints
Xu, Y., Zhao, S., Song, J., Stewart, R., and Ermon, S · 2020
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Learning invariant representations for reinforcement learning without reconstruction
Zhang, A., McAllister, R., Calandra, R., Gal, Y., and Levine, S · 2020
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