  The rise of vehicle automation has generated significant interest in the
potential role of future automated vehicles (AVs). In particular, in highly
dense traffic settings, AVs are expected to serve as congestion-dampeners,
mitigating the presence of instabilities that arise from various sources.
However, in many applications, such maneuvers rely heavily on non-local sensing
or coordination by interacting AVs, thereby rendering their adaptation to
real-world settings a particularly difficult challenge. To address this
challenge, this paper examines the role of imitation learning in bridging the
gap between such control strategies and realistic limitations in communication
and sensing. Treating one such controller as an "expert", we demonstrate that
imitation learning can succeed in deriving policies that, if adopted by 5% of
vehicles, may boost the energy-efficiency of networks with varying traffic
conditions by 15% using only local observations. Results and code are available
online at https://sites.google.com/view/il-traffic/home.
