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Adaptive path-integral autoencoder: representation learning and planning for dynamical systems*This article is an updated version of: Ha J-S, Park Y-J, Chae H-J, Park S-S and Choi H-L 2018 Adaptive path-integral autoencoders: representation learning and planning for dynamical systems Advances in Neural Information Processing Systems vol 31, ed S Bengio et al (Red Hook, NY: Curran Associates, Inc.) pp 8927–38. (20th December 2019)
Record Type:
Journal Article
Title:
Adaptive path-integral autoencoder: representation learning and planning for dynamical systems*This article is an updated version of: Ha J-S, Park Y-J, Chae H-J, Park S-S and Choi H-L 2018 Adaptive path-integral autoencoders: representation learning and planning for dynamical systems Advances in Neural Information Processing Systems vol 31, ed S Bengio et al (Red Hook, NY: Curran Associates, Inc.) pp 8927–38. (20th December 2019)
Main Title:
Adaptive path-integral autoencoder: representation learning and planning for dynamical systems*This article is an updated version of: Ha J-S, Park Y-J, Chae H-J, Park S-S and Choi H-L 2018 Adaptive path-integral autoencoders: representation learning and planning for dynamical systems Advances in Neural Information Processing Systems vol 31, ed S Bengio et al (Red Hook, NY: Curran Associates, Inc.) pp 8927–38.
Abstract: We present a representation learning algorithm that learns a low-dimensional latent dynamical system from high-dimensional sequential raw data, e.g. video. The framework builds upon recent advances in amortized inference methods that use both an inference network and a refinement procedure to output samples from a variational distribution given an observation sequence, and takes advantage of the duality between control and inference to approximately solve the intractable inference problem using the path integral control approach. The learned dynamical model can be used to predict and plan the future states; we also present the efficient planning method that exploits the learned low-dimensional latent dynamics. Numerical experiments show that the proposed path-integral control based variational inference method leads to tighter lower bounds in statistical model learning of sequential data. The supplementary video (https://youtu.be/xCp35crUoLQ ) and the implementation code (https://github.com/yjparkLiCS/18-NIPS-APIAE ) are available online.