  Deep Reinforcement Learning has proved to be able to solve many control tasks
in different fields, but the behavior of these systems is not always as
expected when deployed in real-world scenarios. This is mainly due to the lack
of domain adaptation between simulated and real-world data together with the
absence of distinction between train and test datasets. In this work, we
investigate these problems in the autonomous driving field, especially for a
maneuver planning module for roundabout insertions. In particular, we present a
system based on multiple environments in which agents are trained
simultaneously, evaluating the behavior of the model in different scenarios.
Finally, we analyze techniques aimed at reducing the gap between simulated and
real-world data showing that this increased the generalization capabilities of
the system both on unseen and real-world scenarios.
