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This paper presents DeepKoCo, a novel model-based agent that learns a latent Koopman representation from images.
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C. Gelada, S. Kumar, J. Buckman, O. Nachum, and M. G. Bellemare, “DeepMDP: Learning continuous latent space models for representation learning,” 36th International Conference on Machine Learning (ICML) , pp. 3802–3826, 2019
2019
Later among the works it cites.
D. Hafner, T. Lillicrap, I. Fischer, R. Villegas, D. Ha, H. Lee, and J. Davidson, “Learning latent dynamics for planning from pixels,” in International Conference on Machine Learning (ICML) . PMLR, 2019, pp. 2555–2565
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2020
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B. Lusch, J. N. Kutz, and S. L. Brunton, “Deep learning for universal linear embeddings of nonlinear dynamics,” Nature Communications , vol. 9, no. 1, pp. 1–10, dec 2018
2018
Cited alongside, same era.
D. Ha and J. Schmidhuber, “World Models,” arXiv preprint arXiv:1803.10122 , 2018
2018
Cited alongside, same era.
G. Mamakoukas, M. Castano, X. Tan, and T. Murphey, “Local koopman operators for data-driven control of robotic systems,” in Proceedings of Robotics: Science and Systems (RSS) , 2019
2019
Cited alongside, same era.
D. Bruder, B. Gillespie, C. D. Remy, and R. Vasudevan, “Modeling and control of soft robots using the koopman operator and model predictive control,” in Proceedings of Robotics: Science and Systems (RSS) , 2019
2019
Cited alongside, same era.
Closest in time.
Y. Li, H. He, J. Wu, D. Katabi, and A. Torralba, “Learning compositional koopman operators for model-based control,” in International Conference on Learning Representations (ICLR) , 2020
2020
Closest in time.
M. Korda and I. Mezic, “Optimal construction of koopman eigenfunctions for prediction and control,” IEEE Transactions on Automatic Control , 2020
2020
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B. van der Heijden, L. Ferranti, J. Kober, and R. Babuška, “Supplementary video material of DeepKoCo simulations,” https://youtu.be/75lOuQyHBmQ
2021
Closest in time.