AI RESEARCH
Adaptive Control in Autonomous Driving via Real-Time Recurrent RL
arXiv CS.LG
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ArXi:2602.02236v4 Announce Type: replace-cross We study online fine-tuning of pretrained control policies for autonomous driving using Real-Time Recurrent Reinforcement Learning (RTRRL), a memory-efficient algorithm that updates policy parameters at every time step without backpropagation through time. We extend RTRRL to LrcSSM, a recently proposed nonlinear diagonal state-space model, and combine offline behavioral cloning with online RTRRL fine-tuning to adapt policies to distribution shifts at deployment.