Learning State Representations in Complex Systems with Multimodal Data

Pavel Solovev, Vladimir Aliev, Pavel Ostyakov, Gleb Sterkin, Elizaveta Logacheva, Stepan Troeshestov, Roman Suvorov, Anton Mashikhin, Oleg Khomenko, Sergey I. Nikolenko

Representation learning becomes especially important for complex systems with multimodal data sources such as cameras or sensors. Recent advances in reinforcement learning and optimal control make it possible to design control algorithms on these latent representations, but the field still lacks a large-scale standard dataset for unified comparison. In this work, we present a large-scale dataset and evaluation framework for representation learning for the complex task of landing an airplane. We implement and compare several approaches to representation learning on this dataset in terms of the quality of simple supervised learning tasks and disentanglement scores. The resulting representations can be used for further tasks such as anomaly detection, optimal control, model-based reinforcement learning, and other applications.

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