  This article presents the first field deployment of a multi-agent
reinforcement-learning (MARL) based variable speed limit (VSL) control system
on the I-24 freeway near Nashville, Tennessee. We describe how we train MARL
agents in a traffic simulator and directly deploy the simulation-based policy
on a 17-mile stretch of Interstate 24 with 67 VSL controllers. We use invalid
action masking and several safety guards to ensure the posted speed limits
satisfy the real-world constraints from the traffic management center and the
Tennessee Department of Transportation. Since the time of launch of the system
through April, 2024, the system has made approximately 10,000,000 decisions on
8,000,000 trips. The analysis of the controller shows that the MARL policy
takes control for up to 98% of the time without intervention from safety
guards. The time-space diagrams of traffic speed and control commands
illustrate how the algorithm behaves during rush hour. Finally, we quantify the
domain mismatch between the simulation and real-world data and demonstrate the
robustness of the MARL policy to this mismatch.
