Smart mobility intelligent traffic services have become critical in intelligent transportation systems (ITS). This involves using advanced sensors and controllers and the ability to respond to real-time traffic situations at intersections, alleviate congestion, and generate policies to prevent traffic jams. Deep reinforcement learning (DRL) provides a natural framework for processing tasks. In DRL, each intersection can control itself and coordinate with neighbors to achieve optimal network-wide policies. On the other hand, comparing approaches remains a challenging task due to the existence of numerous possible configurations. This research performs a critical comparison of various traffic controllers in the literature. Hence, using a nonlinear approximator for coordination mechanisms and enhancing observability at each intersection are key performance drivers.