2025
RAD: Training an End-to-End Driving Policy via Large-Scale 3DGS-based Reinforcement Learning
NeurIPS 2025poster
Existing end-to-end autonomous driving (AD) algorithms typically follow the Imitation Learning (IL) paradigm, which faces challenges such as causal confusion and an open-loop gap. In this work, we propose RAD, a 3DGS-based closed-loop Reinforcement Learning (RL) framework for end-to-end Autonomous D…