  Traffic congestion is a persistent problem in our society. Previous methods
for traffic control have proven futile in alleviating current congestion levels
leading researchers to explore ideas with robot vehicles given the increased
emergence of vehicles with different levels of autonomy on our roads. This
gives rise to mixed traffic control, where robot vehicles regulate human-driven
vehicles through reinforcement learning (RL). However, most existing studies
use precise observations that require domain expertise and hand engineering for
each road network's observation space. Additionally, precise observations use
global information, such as environment outflow, and local information, i.e.,
vehicle positions and velocities. Obtaining this information requires updating
existing road infrastructure with vast sensor environments and communication to
potentially unwilling human drivers. We consider image observations, a modality
that has not been extensively explored for mixed traffic control via RL, as the
alternative: 1) images do not require a complete re-imagination of the
observation space from environment to environment; 2) images are ubiquitous
through satellite imagery, in-car camera systems, and traffic monitoring
systems; and 3) images only require communication to equipment. In this work,
we show robot vehicles using image observations can achieve competitive
performance to using precise information on environments, including ring,
figure eight, intersection, merge, and bottleneck. In certain scenarios, our
approach even outperforms using precision observations, e.g., up to 8% increase
in average vehicle velocity in the merge environment, despite only using local
traffic information as opposed to global traffic information.
