The authors introduce Enactor, an actor-centric generative model designed for closed-loop microsimulation at signalized intersections. Unlike traditional simulators that rely on hand-crafted rules or short-horizon predictors, Enactor focuses on vehicle dynamics while treating pedestrians as contextual influences. The architecture encodes dynamic actors and lane polylines in polar coordinates relative to the intersection center. A transformer with separate spatial and temporal attention blocks predicts a distribution over each actor's next-step motion parameters. Training employs a closed-loop curriculum, exposing the model to its own predictions to ensure stability during simulation. Evaluations on two intersection geometries show Enactor recovers SUMO data generator distributions with significantly lower KL divergence than transformer baselines. The model also reduces red-light violations by more than an order of magnitude and outperforms constant-velocity baselines on real-world field data.