Before revision:  By now only little work focuses on the compactness of the provided state, which is a necessary condition to successfully and efficiently train a navigation agent. Our approach works in three stages. First, we propose a novel preprocessing of the distance measurements and compute a local , egocentric, binary grid map based on the current range measurements. We then autoencode the local map using a variational autoencoder, where the latent space serves as state representation . An important key for a compact and , at the same time, meaningful representation is the degree of disentanglement, which describes the correlation between each latent dimension. Therefore, we finally apply state-of-the-art disentangling methods to improve the representation power . Furthermore, we investige the possibilities of incorporating time-dependent information into the latent space. In particular, we incorporate the relation of consecutive scans, especially ego-motion, by applying a memory model.
After revision:  To achieve high reconstruction power of our autoencoding pipeline, we propose an - in the context of autoencoding 2D-lidar scans - novel preprocessing into a local binary occupancy image. This has no additional requirements, neither self-localization nor robust mapping, and therefore can be applied in any setting and easily transferred from simulation in real-world. In a second stage, we show the usage of the compact state representation generated by our autoencoding pipeline in a simplistic navigation task and expose the pitfall that increased reconstruction power will always lead to an improved performance.