  Transportation and traffic are currently undergoing a rapid increase in terms
of both scale and complexity. At the same time, an increasing share of traffic
participants are being transformed into agents driven or supported by
artificial intelligence resulting in mixed-intelligence traffic. This work
explores the implications of distributed decision-making in mixed-intelligence
traffic. The investigations are carried out on the basis of an online-simulated
highway scenario, namely the MIT \emph{DeepTraffic} simulation. In the first
step traffic agents are trained by means of a deep reinforcement learning
approach, being deployed inside an elitist evolutionary algorithm for
hyperparameter search. The resulting architectures and training parameters are
then utilized in order to either train a single autonomous traffic agent and
transfer the learned weights onto a multi-agent scenario or else to conduct
multi-agent learning directly. Both learning strategies are evaluated on
different ratios of mixed-intelligence traffic. The strategies are assessed
according to the average speed of all agents driven by artificial intelligence.
Traffic patterns that provoke a reduction in traffic flow are analyzed with
respect to the different strategies.
