Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement
  Learning Systems for Multi-Agent Dense Traffic Navigation   We present a traffic simulation named DeepTraffic where the planning systems
for a subset of the vehicles are handled by a neural network as part of a
model-free, off-policy reinforcement learning process. The primary goal of
DeepTraffic is to make the hands-on study of deep reinforcement learning
accessible to thousands of students, educators, and researchers in order to
inspire and fuel the exploration and evaluation of deep Q-learning network
variants and hyperparameter configurations through large-scale, open
competition. This paper investigates the crowd-sourced hyperparameter tuning of
the policy network that resulted from the first iteration of the DeepTraffic
competition where thousands of participants actively searched through the
hyperparameter space.

7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
17
The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org