Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Reinforcement Learning for Autonomous Driving: A Survey   With the development of deep representation learning, the domain of
reinforcement learning (RL) has become a powerful learning framework now
capable of learning complex policies in high dimensional environments. This
review summarises deep reinforcement learning (DRL) algorithms and provides a
taxonomy of automated driving tasks where (D)RL methods have been employed,
while addressing key computational challenges in real world deployment of
autonomous driving agents. It also delineates adjacent domains such as behavior
cloning, imitation learning, inverse reinforcement learning that are related
but are not classical RL algorithms. The role of simulators in training agents,
methods to validate, test and robustify existing solutions in RL are discussed.

34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
10
The categories of this paper are: q-bio