  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.
