Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Autonomous Quadrotor Landing using Deep Reinforcement Learning   Landing an unmanned aerial vehicle (UAV) on a ground marker is an open
problem despite the effort of the research community. Previous attempts mostly
focused on the analysis of hand-crafted geometric features and the use of
external sensors in order to allow the vehicle to approach the land-pad. In
this article, we propose a method based on deep reinforcement learning that
only requires low-resolution images taken from a down-looking camera in order
to identify the position of the marker and land the UAV on it. The proposed
approach is based on a hierarchy of Deep Q-Networks (DQNs) used as high-level
control policy for the navigation toward the marker. We implemented different
technical solutions, such as the combination of vanilla and double DQNs, and a
partitioned buffer replay. Using domain randomization we trained the vehicle on
uniform textures and we tested it on a large variety of simulated and
real-world environments. The overall performance is comparable with a
state-of-the-art algorithm and human pilots.

14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg