Stability-Preserving Reinforcement Learning for Heterogeneity-Aware Composite Max-Pressure Traffic Signal Control

Reinforcement learning (RL) provides a flexible mechanism for adapting traffic signal control to heterogeneous traffic and sensing conditions, but directly learned signal actions do not automatically inherit the analytical properties of structured traffic-control methods. This paper proposes Com-MP,