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[2602.02236] Online Fine-Tuning of Pretrained Controllers for Autonomous Driving via Real-Time Recurrent RL
Summary
The paper discusses the use of Real-Time Recurrent Reinforcement Learning (RTRRL) for fine-tuning pretrained controllers in autonomous driving, addressing challenges posed by environmental changes.
Why It Matters
This research is significant as it tackles the limitations of fixed policies in autonomous systems, which can degrade in performance due to dynamic environments. The proposed RTRRL approach enhances adaptability, potentially improving the reliability of autonomous driving technologies in real-world applications.
Key Takeaways
- RTRRL can effectively fine-tune pretrained policies for autonomous driving.
- The method improves performance in changing environments and tasks.
- Demonstrated effectiveness in both simulated and real-world scenarios.
Computer Science > Robotics arXiv:2602.02236 (cs) [Submitted on 2 Feb 2026 (v1), last revised 17 Feb 2026 (this version, v3)] Title:Online Fine-Tuning of Pretrained Controllers for Autonomous Driving via Real-Time Recurrent RL Authors:Julian Lemmel, Felix Resch, Mónika Farsang, Ramin Hasani, Daniela Rus, Radu Grosu View a PDF of the paper titled Online Fine-Tuning of Pretrained Controllers for Autonomous Driving via Real-Time Recurrent RL, by Julian Lemmel and 5 other authors View PDF HTML (experimental) Abstract:Deploying pretrained policies in real-world applications presents substantial challenges that fundamentally limit the practical applicability of learning-based control systems. When autonomous systems encounter environmental changes in system dynamics, sensor drift, or task objectives, fixed policies rapidly degrade in performance. We show that employing Real-Time Recurrent Reinforcement Learning (RTRRL), a biologically plausible algorithm for online adaptation, can effectively fine-tune a pretrained policy to improve autonomous agents' performance on driving tasks. We further show that RTRRL synergizes with a recent biologically inspired recurrent network model, the Liquid-Resistance Liquid-Capacitance RNN. We demonstrate the effectiveness of this closed-loop approach in a simulated CarRacing environment and in a real-world line-following task with a RoboRacer car equipped with an event camera. Subjects: Robotics (cs.RO); Machine Learning (cs.LG); Neural and Evol...
