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Model-based methods have significantly contributed to distinguishing task-irrelevant distractors for visual control.
Integrated architectures for learning, planning, and reacting based on approximating dynamic programming
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The kinetics human action video dataset
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Du, S., Krishnamurthy, A., Jiang, N., Agarwal, A., Dudik, M., and Langford, J · 2019
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Temporal cycle-consistency learning
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Imitating latent policies from observation
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When to trust your model: Model-based policy optimization
Janner, M., Fu, J., Zhang, M., and Levine, S · 2019
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Learning correspondence from the cycle-consistency of time
Wang, X., Jabri, A., and Efros, A. A · 2019
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Dream to control: Learning behaviors by latent imagination
Hafner, D., Lillicrap, T. P., Ba, J., and Norouzi, M · 2020
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Laskin, M., Srinivas, A., and Abbeel, P · 2020
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Data-efficient reinforcement learning with self-predictive representations
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Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
Information prioritization through empowerment in visual model-based RL
Bharadhwaj, H., Babaeizadeh, M., Erhan, D., and Levine, S · 2022
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Deep hierarchical planning from pixels
Hafner, D., Lee, K.-H., Fischer, I., and Abbeel, P · 2022
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Action-sufficient state representation learning for control with structural constraints
Huang, B., Lu, C., Leqi, L., Hernández-Lobato, J. M., Glymour, C., Schölkopf, B., and Zhang, K · 2022
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A survey on model-based reinforcement learning
Luo, F.-M., Xu, T., Lai, H., Chen, X.-H., Zhang, W., and Yu, Y · 2022
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Iso-dream: Isolating and leveraging noncontrollable visual dynamics in world models
Pan, M., Zhu, X., Wang, Y., and Yang, X · 2022
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Yarats, D., Kostrikov, I., and Fergus, R · 2020
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Mopo: Model-based offline policy optimization
Yu, T., Thomas, G., Yu, L., Ermon, S., Zou, J. Y., Levine, S., Finn, C., and Ma, T · 2020
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Learning invariant representations for reinforcement learning without reconstruction
Zhang, A., McAllister, R. T., Calandra, R., Gal, Y., and Levine, S · 2020
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LASER: learning a latent action space for efficient reinforcement learning
Allshire, A., Martín-Martín, R., Lin, C., Manuel, S., Savarese, S., and Garg, A · 2021
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Provable rl with exogenous distractors via multistep inverse dynamics
Efroni, Y., Misra, D., Krishnamurthy, A., Agarwal, A., and Langford, J · 2021
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Learning task informed abstractions
Fu, X., Yang, G., Agrawal, P., and Jaakkola, T. S · 2021
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Mastering Atari with discrete world models
Hafner, D., Lillicrap, T. P., Norouzi, M., and Ba, J · 2021
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Denoised MDPs: Learning world models better than the world itself
Wang, T., Du, S., Torralba, A., Isola, P., Zhang, A., and Tian, Y · 2022
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Mastering diverse domains through world models
Hafner, D., Pasukonis, J., Ba, J., and Lillicrap, T · 2023
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Disentanglement via latent quantization
Hsu, K., Dorrell, W., Whittington, J. C., Wu, J., and Finn, C · 2023
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Efficient planning in a compact latent action space
Jiang, Z., Zhang, T., Janner, M., Li, Y., Rocktäschel, T., Grefenstette, E., and Tian, Y · 2023
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Segment anything
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., et al · 2023
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Structured world models from human videos
Mendonca, R., Bahl, S., and Pathak, D · 2023
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Model-based reinforcement learning: A survey
Moerland, T. M., Broekens, J., Plaat, A., Jonker, C. M., et al · 2023
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Semail: eliminating distractors in visual imitation via separated models
Wan, S., Wang, Y., Shao, M., Chen, R., and Zhan, D.-C · 2023
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Become a proficient player with limited data through watching pure videos
Ye, W., Zhang, Y., Abbeel, P., and Gao, Y · 2023
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Genie: Generative interactive environments
Bruce, J., Dennis, M., Edwards, A., Parker-Holder, J., Shi, Y., Hughes, E., Lai, M., Mavalankar, A., Steigerwald, R., Apps, C., et al · 2024
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Learning to act without actions
Schmidt, D. and Jiang, M · 2024
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