Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The Pitfall of More Powerful Autoencoders in Lidar-Based Navigation   The benefit of pretrained autoencoders for reinforcement learning in
comparison to training on raw observations is already known [1]. In this paper,
we address the generation of a compact and information-rich state
representation. In particular, we train a variational autoencoder for 2D-lidar
scans to use its latent state for reinforcement learning of navigation tasks.
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. We implemented our approach in python using tensorflow. Our
datasets are simulated with pybullet as well as recorded using a slamtec
rplidar A3. The experiments show the significantly improved reconstruction
capabilities of our approach for 2D-lidar scans w.r.t. the state of the art.
However, as we demonstrate in the experiments the impact on reinforcement
learning in lidar-based navigation tasks is non-predictable when improving the
latent state representation generated by an autoencoding pipeline. This is
surprising and needs to be taken into account during the process of optimizing
a pretrained autoencoder for reinforcement learning tasks.

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The categories of this paper are: quant-ph
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The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
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The categories of this paper are: atom-ph
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The categories of this paper are: physics
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The categories of this paper are: hep-th
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The categories of this paper are: stat
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The categories of this paper are: hep-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-fin
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The categories of this paper are: hep-lat
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The categories of this paper are: solv-int
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The categories of this paper are: comp-gas