  Traffic light timing optimization is still an active line of research despite
the wealth of scientific literature on the topic, and the problem remains
unsolved for any non-toy scenario. One of the key issues with traffic light
optimization is the large scale of the input information that is available for
the controlling agent, namely all the traffic data that is continually sampled
by the traffic detectors that cover the urban network. This issue has in the
past forced researchers to focus on agents that work on localized parts of the
traffic network, typically on individual intersections, and to coordinate every
individual agent in a multi-agent setup. In order to overcome the large scale
of the available state information, we propose to rely on the ability of deep
Learning approaches to handle large input spaces, in the form of Deep
Deterministic Policy Gradient (DDPG) algorithm. We performed several
experiments with a range of models, from the very simple one (one intersection)
to the more complex one (a big city section).
