Distributed traffic light control at uncoupled intersections with
  real-world topology by deep reinforcement learning
  This work examines the implications of uncoupled intersections with local
real-world topology and sensor setup on traffic light control approaches.
Control approaches are evaluated with respect to: Traffic flow, fuel
consumption and noise emission at intersections.
  The real-world road network of Friedrichshafen is depicted, preprocessed and
the present traffic light controlled intersections are modeled with respect to
state space and action space.
  Different strategies, containing fixed-time, gap-based and time-based control
approaches as well as our deep reinforcement learning based control approach,
are implemented and assessed. Our novel DRL approach allows for modeling the
TLC action space, with respect to phase selection as well as selection of
transition timings. It was found that real-world topologies, and thus
irregularly arranged intersections have an influence on the performance of
traffic light control approaches. This is even to be observed within the same
intersection types (n-arm, m-phases). Moreover we could show, that these
influences can be efficiently dealt with by our deep reinforcement learning
based control approach.
