Adaptive Tuning of Parameterized Traffic Controllers via Multi-Agent Reinforcement Learning

ArXi:2512.07417v2 Announce Type: replace Effective traffic control is essential for mitigating congestion in transportation networks. Conventional traffic management strategies, including route guidance and ramp metering, often rely on state feedback controllers, which are used for their simplicity and reactivity; however, they lack the adaptability required to cope with complex and time-varying traffic dynamics.