  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.
