Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Behaviorally Diverse Traffic Simulation via Reinforcement Learning   Traffic simulators are important tools in autonomous driving development.
While continuous progress has been made to provide developers more options for
modeling various traffic participants, tuning these models to increase their
behavioral diversity while maintaining quality is often very challenging. This
paper introduces an easily-tunable policy generation algorithm for autonomous
driving agents. The proposed algorithm balances diversity and driving skills by
leveraging the representation and exploration abilities of deep reinforcement
learning via a distinct policy set selector. Moreover, we present an algorithm
utilizing intrinsic rewards to widen behavioral differences in the training. To
provide quantitative assessments, we develop two trajectory-based evaluation
metrics which measure the differences among policies and behavioral coverage.
We experimentally show the effectiveness of our methods on several challenging
intersection scenes.

24
The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
16
The categories of this paper are: stat
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con