  In this work, we study adaptive data-guided traffic planning and control
using Reinforcement Learning (RL). We shift from the plain use of classic
methods towards state-of-the-art in deep RL community. We embed several recent
techniques in our algorithm that improve the original Deep Q-Networks (DQN) for
discrete control and discuss the traffic-related interpretations that follow.
We propose a novel DQN-based algorithm for Traffic Control (called TC-DQN+) as
a tool for fast and more reliable traffic decision-making. We introduce a new
form of reward function which is further discussed using illustrative examples
with comparisons to traditional traffic control methods.
