Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Reinforcement Learning Based Approach for Automated Lane Change
  Maneuvers   Lane change is a crucial vehicle maneuver which needs coordination with
surrounding vehicles. Automated lane changing functions built on rule-based
models may perform well under pre-defined operating conditions, but they may be
prone to failure when unexpected situations are encountered. In our study, we
proposed a Reinforcement Learning based approach to train the vehicle agent to
learn an automated lane change behavior such that it can intelligently make a
lane change under diverse and even unforeseen scenarios. Particularly, we
treated both state space and action space as continuous, and designed a
Q-function approximator that has a closed- form greedy policy, which
contributes to the computation efficiency of our deep Q-learning algorithm.
Extensive simulations are conducted for training the algorithm, and the results
illustrate that the Reinforcement Learning based vehicle agent is capable of
learning a smooth and efficient driving policy for lane change maneuvers.

10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th