R²Drive: Reinforcement-Learned Reactive Agents in a Closed-Loop 3D Driving Simulator

We present R²Drive, a closed-loop multi-agent dynamics framework for sensor-level autonomous driving simulation with reactive surrounding vehicles. R²Drive couples style-specific RL traffic policies with a 3D Gaussian simulator for adaptive behavior and closed-loop planning. The simulator preserves geometric fidelity (34.6 dB PSNR) while supporting distinct safety- and efficiency-oriented policy variants. Each reconstructed circuit is playable in a browser at 12–30 FPS over 1.5–2.0M Gaussians, enabling manual driving, shared-policy multi-agent replay, or an external planner receiving rendered RGB observations. On Waymo, the adapted agents reach 4% collision and 86% goal achievement; on nuPlan, the safety variant transfers zero-shot to 7.6% collision and 98.8% goal achievement. These results bridge photorealistic reconstruction and programmable multi-agent dynamics for closed-loop planner validation.