mlsanityData to 7 Oct 2026

Adaptive Control in Autonomous Driving via Real-Time Recurrent RL

arXiv 2602.02236 ↗First posted 2 Feb 2026cs.RO cs.LG cs.NEcited by 0

On the map: End-to-end autonomous driving → Reinforcement learning for driving

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. Builds on 28 of its 40 references that are in the map. First posted on arXiv 2 Feb 2026.

Builds on

28 of 40 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023A Platform-Agnostic Deep Reinforcement Learning Framework for Effective Sim2Real Transfer towards Autonomous Driving14 Apr
  2. 2022Reinforcement Learning from Simulation to Real World Autonomous Driving using Digital Twin27 Nov

Other references · 26

  1. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  2. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  3. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,401
  4. 2019Event-based Vision: A Surveycited by 1,010
  5. 2023Resurrecting Recurrent Neural Networks for Long Sequencescited by 397
  6. 2018Quantifying Generalization in Reinforcement Learningcited by 345
  7. 2018Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Carscited by 197
  8. 2023Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarkscited by 90
  9. 2023GET: Group Event Transformer for Event-Based Visioncited by 73
  10. 2024Beyond Model Adaptation at Test Time: A Surveycited by 55
  11. 2019A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networkscited by 37
  12. 2021Event-driven Vision and Control for UAVs on a Neuromorphic Chipcited by 33
14 more
  1. 2023Fully neuromorphic vision and control for autonomous drone flightcited by 30
  2. 2023Online learning of long-range dependenciescited by 25
  3. 2018Measuring and Characterizing Generalization in Deep Reinforcement Learningcited by 20
  4. 2026On-the-Fly VLA Adaptation via Test-Time Reinforcement Learningcited by 14
  5. 2025Parallelization of Non-linear State-Space Models: Scaling Up Liquid-Resistance Liquid-Capacitance Networks for Efficient Sequence Modelingcited by 10
  6. 2017CDDT: Fast Approximate 2D Ray Casting for Accelerated Localizationcited by 9
  7. 2024A Survey Analyzing Generalization in Deep Reinforcement Learningcited by 8
  8. 2024Liquid Resistance Liquid Capacitance Networkscited by 7
  9. 2023Real-Time Recurrent Reinforcement Learningcited by 6
  10. 2025Efficient Continual Adaptation of Pretrained Robotic Policy with Online Meta-Learned Adapterscited by 6
  11. 2023Learning with Chemical versus Electrical Synapses -- Does it Make a Difference?cited by 5
  12. 2024Towards Fast Safe Online Reinforcement Learning via Policy Finetuningcited by 3
  13. 2024MMDVS-LF: Multi-Modal Dynamic Vision Sensor and Eye-Tracking Dataset for Line Followingcited by 1
  14. 2025Online Fine-Tuning of Carbon Emission Predictions using Real-Time Recurrent Learning for State Space Modelscited by 1

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  1. 2023Incorporating Recurrent Reinforcement Learning into Model Predictive Control for Adaptive Control in Autonomous Driving
  2. 2023FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing
  3. 2020Deep Reinforcement Learning for Autonomous Driving: A Survey
  4. 2021Offline Reinforcement Learning for Autonomous Driving with Safety and Exploration Enhancement
  5. 2025Pseudo-Expert Regularized Offline RL for End-to-End Autonomous Driving in Photorealistic Closed-Loop Environments
  6. 2022Self-Adaptive Driving in Nonstationary Environments through Conjectural Online Lookahead Adaptation
  7. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learning
  8. 2025A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective