Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploring applications of deep reinforcement learning for real-world
  autonomous driving systems   Deep Reinforcement Learning (DRL) has become increasingly powerful in recent
years, with notable achievements such as Deepmind's AlphaGo. It has been
successfully deployed in commercial vehicles like Mobileye's path planning
system. However, a vast majority of work on DRL is focused on toy examples in
controlled synthetic car simulator environments such as TORCS and CARLA. In
general, DRL is still at its infancy in terms of usability in real-world
applications. Our goal in this paper is to encourage real-world deployment of
DRL in various autonomous driving (AD) applications. We first provide an
overview of the tasks in autonomous driving systems, reinforcement learning
algorithms and applications of DRL to AD systems. We then discuss the
challenges which must be addressed to enable further progress towards
real-world deployment.

36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th