  Coordinating intersections in arterial networks is critical to the
performance of urban transportation systems. Deep reinforcement learning (RL)
has gained traction in traffic control research along with data-driven
approaches for traffic control systems. To date, proposed deep RL-based traffic
schemes control phase activation or duration. Yet, such approaches may bypass
low volume links for several cycles in order to optimize the network-level
traffic flow. Here, we propose a deep RL framework that dynamically adjusts
offsets based on traffic states and preserves the planned phase timings and
order derived from model-based methods. This framework allows us to improve
arterial coordination while maintaining phase order and timing predictability.
Using a validated and calibrated traffic model, we trained the policy of a deep
RL agent that aims to reduce travel delays in the network. We evaluated the
resulting policy by comparing its performance against the phase offsets
deployed along a segment of Huntington Drive in the city of Arcadia. The
resulting policy dynamically readjusts phase offsets in response to changes in
traffic demand. Simulation results show that the proposed deep RL agent
outperformed the baseline on average, effectively reducing delay time by 13.21%
in the AM Scenario, 2.42% in the Noon scenario, and 6.2% in the PM scenario
when offsets are adjusted in 15-minute intervals. Finally, we also show the
robustness of our agent to extreme traffic conditions, such as demand surges in
off-peak hours and localized traffic incidents
