実時間リカレント強化学習による自動運転の適応制御
Adaptive Control in Autonomous Driving via Real-Time Recurrent RL
メモリ効率の良いRTRRLで事前学習済み運転ポリシーをオンライン微調整し、LrcSSMと組み合わせてシミュレーションと実機イベントカメラで適応制御を実証した。
詳しい要約
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著者: Julian Lemmel, Felix Resch, Mónika Farsang, Ramin Hasani, Daniela Rus, Radu Grosu
分類: cs.RO, cs.LG, cs.NE, cs.SY, eess.SY
原文アブストラクト
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 support 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. We validate the approach in the CarRacing simulation and on a 1:10-scale RoboRacer platform equipped with an event camera, where a pretrained policy is fine-tuned online during real-world line-following. To our knowledge, this is the first demonstration of online RL fine-tuning with event-camera observations on standard (non-spiking) hardware in closed-loop control. LrcSSM-based policies improve fastest and most consistently across both settings.
