RL for Autonomous Driving

RL: Reinforcement Learning

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Overview

The field studies how vehicles can learn driving decisions and controls through rewards and interaction, including how to navigate traffic, avoid hazards, and coordinate with other road users. Methods range from PPO, SAC, and DQN to model-based and world-model learning, multi-agent reinforcement learning, and combinations of imitation learning with diffusion or vision-language-action planners. Safety is addressed through reward design, expert guidance, control barrier functions, and reachability constraints, with evaluation commonly using CARLA, nuPlan, NAVSIM, and Bench2Drive. A central challenge is achieving sample-efficient learning that remains safe and reliable in closed-loop, unfamiliar, and real-world conditions, where simulator fidelity and trade-offs among safety, progress, and comfort matter.

Latest papers

CardsList

120 papers21 papers in the last four weeks, up 200% on the four weeks before. 0.1% of all new papers.

  1. Hyperparameter and Neural Architecture Search via Evolutionary Optimization for Reinforcement Learning: HPNAS-RL

    Oct 27, 2026Nihal Acharya Adde, Hanno GottschalkNeural Architecture SearchReinforcement Learning

    Volkswagen Group Innovation, Volkswagen AG, Berliner Ring 2, Wolfsburg, 38440, Lower Saxony, Germany. · Institute of Mathematics, TU Berlin, Straße des 17. Juni 136, Berlin, 10623, Berlin, Germany.

  2. IntNet: A Communication-Driven Multi-Agent Reinforcement Learning Framework for Cooperative Autonomous Driving

    Oct 25, 2026Leandro Parada, Kevin Yu, Panagiotis AngeloudisRL for Autonomous DrivingCooperative MARL

    L. Parada, K. Yu, and P. Angeloudis are with the Centre for Transport Studies, Department of Civil and Environmental Engineering, Imperial College London, UK

  3. Computer Vision Navigation for an Autonomous Vehicle Model Using Reinforcement Learning

    Oct 24, 2026Dinis Fernandes, Tiago Ribeiro, António Ribeiro +3RL for Autonomous DrivingRL Control

    Lab. Automação e Robótica University of Minho Guimarães, Portugal

  4. Human-Feedback-Guided Multi-Agent Inverse Reinforcement Learning for Interactive Driving Behavior Modeling

    Oct 24, 2026Longhao Yan, Jinhao Liang, Kaidi YangReinforcement LearningRL for Autonomous Driving

    Department of Civil and Environmental Engineering, National University of Singapore, Singapore 117576.

  5. DriveMLP: When Training Strategy Outperforms Model Complexity in Autonomous Driving

    Oct 21, 2026Alessandro Canevaro, Julian Schmidt, Hang Yu +4Autonomous Driving PlanningImitation Learning

    Mercedes-Benz AG, Research & Development, Sindelfingen, Germany · University of Tübingen, Tübingen, Germany · Karlsruhe Institute of Technology, ITIV, Karlsruhe, Germany +2

  6. Prescribed Performance Guided Safe Reinforcement Learning for Roundabout Navigation of Autonomous Vehicles

    Oct 20, 2026Wenhan Li, Yangrui Zhang, Hang Zhou +2Autonomous Driving PlanningRL for Autonomous Driving

    Robotics and Autonomous Systems Thrust, Systems Hub, the Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong Province, China

  7. Simulation Priors for Rapid Adaptation in Model-Based RL for Autonomous Racing

    Oct 19, 2026Adrian Remonda, Jiajun Xi, Nicklas Hansen +5RL for Autonomous DrivingModel-Based RL

    Toyota Research Institute, Los Altos, CA, USA. · University of California San Diego (UCSD), La Jolla, CA, USA.

  8. GRID-Planner: Graph-Structured Relational Reward Shaping for Diffusion Planning

    Oct 15, 2026Zhao Yang, Zezhong Qian, Yinan Shi +2Autonomous Driving PlanningDiffusion-Based Planning

    Xi’an Jiaotong University, Xi’an, China · Cytoderm Intelligent Technology, Beijing, China

  9. Race to Recovery: Designing Advanced Safety Systems for Autonomous Driving via Learning Autonomous Racing

    Oct 14, 2026Teodor Ilie, Kleber Cabral, Sidney GivigiRL for Autonomous DrivingReinforcement Learning

    School of Computing, Queen’s University, Kingston, ON K7L 2N8, Canada

  10. Uncertainty-Driven Self-Play for Robust Multi-Agent Driving Policies

    Oct 14, 2026Sajad Marvi, Julian Schmidt, Alessandro Canevaro +2Uncertainty-Aware ExplorationAutonomous Driving Planning

    Mercedes-Benz AG, Germany. · Department of Computer Science, University of Freiburg, Germany.

  11. Comprehensive Comparisons of Reinforcement Learning Algorithms in Autonomous Driving

    Oct 14, 2026Dechen Xie, Mushuang LiuRL for Autonomous DrivingAutonomous Driving

    Department of Mechanical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24060, USA

  12. Beyond Safe Execution: Safe Online Reinforcement Learning for Autonomous Driving with Value Calibration and Adaptive Guidance

    Oct 13, 2026Zeqiao Li, Yijing Wang, Haoyu Wang +2RL for Autonomous DrivingSafe RL

  13. XAI-Guided Fourier Domain Generalisation for Pure-Simulation Sim-to-Real Deep Reinforcement Learning

    Oct 12, 2026Wei Yao, Chuyao Wang, Ziwei Wang +1Sim-to-Real TransferDomain Generalization

    City St George’s, University of London, London, United Kingdom

  14. SimVTA: Simulation-Grounded Video-Trajectory Alignment for Driving World Action Models

    Oct 9, 2026Changhui Jing, Haochen Tian, Hongchen Li +8World Models for Autonomous DrivingAutonomous Driving Planning

    Shanghai Innovation Institute · OpenDriveLab at The University of Hong Kong · Tongji University +2

  15. Collective Autonomous Vehicle Routing Needs Social Awareness for Coexistence with Human Drivers

    Oct 9, 2026Anastasia Psarou, Mikolaj Rams, Blazej Torbus +1Vehicle RoutingIntelligent Transportation Systems

    Faculty of Mathematics and Computer Science, Jagiellonian University Kraków, Poland

  16. Lamarck's Driving School: Discovering Autonomous Driving Training Strategies through Evolutionary Competition

    Oct 8, 2026Yichun Ye, He Zhang, Ye Tian +1Curriculum RLAutonomous Driving Scenario Generation

    Department of Traffic Engineering and the Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai, China · School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, Sichuan, China

  17. Meta-Multi-Agent Reinforcement Learning for Fast Adaptation of Interactive Policies with Applications to Autonomous Driving

    Sep 30, 2026Huiwen Yan, Kyriakos G. Vamvoudakis, Mushuang LiuMeta-Reinforcement LearningRL for Autonomous Driving

    Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA 24061, USA · Daniel Guggenheim School of Aerospace Engineering, Georgia Tech, Atlanta, GA 30332, USA

  18. Vision-Language-Action Autonomous Driving Agent with Language-based Memory

    Sep 29, 2026Kai Yan, Xiangyu Chen, Yulong Cao +9Memory-Augmented VLMsVLMs for Autonomous Driving

    University of Illinois Urbana-Champaign · NVIDIA

  19. Brain-SAD: A Brain-Inspired Safe Autonomous Driving Control Framework with Dynamic Fear-Oriented Constraint on Dual-Policy

    Sep 29, 2026Huan Rong, Chao Yin, Anouar Imel +2Constrained RLRL for Autonomous Driving

    School of Artificial Intelligence, Nanjing University of Information Science & Technology, Jiangsu, Nanjing, 210-044, China · School of Information and Communication Engineering, University of Electronic Science and Technology of China, Sichuan, Chengdu, 611731, China · School of Software, Nanjing University of Information Science & Technology, 210044, Jiangsu, China +1

  20. RoXDrive: Closed-Loop Reinforcement Learning for End-to-End Autonomous Driving via Action-Faithful Rollouts

    Sep 29, 2026Hongbin Lin, Chaoda Zheng, Yiming Yang +11End-to-End Autonomous DrivingAction-Conditioned World Models

    The Chinese University of Hong Kong, Shenzhen · XPeng Motors · Xidian University

  21. RefineDrive: Reliable Failure-Guided Learning for Vision-Language-Action Driving

    Sep 28, 2026Zhe Sun, Ziyi Luo, Yehao Lu +2VLMs for Autonomous DrivingAutonomous Driving Planning

    College of Computer Science and Technology, Zhejiang University, Hangzhou, China · Yinwang Intelligent Technology Co., Ltd.

  22. Vision--Language Signals in Constrained RL: Safety Gains Without Anticipation

    Sep 28, 2026Samuel Tetteh, Cody FlemingConstrained RLRL for Autonomous Driving

    Iowa State University Ames, Iowa, USA

  23. Sometimes You Gotta Run Before You Can Walk: Run-then-Walk Scheduling Strategy for VLM Autonomous Driving

    Sep 22, 2026Yuqi Ye, Shangkun Sun, Junhong Lin +6VLMs for Autonomous DrivingGroup Relative Policy Optimization

    Peking University · Minieye Technology Co., Ltd · Fuzhou University

  24. MILER: Semantic Mid-Level Representation for Sim-to-Real Reinforcement Learning in Unstructured Autonomous Driving

    Sep 17, 2026Thomas Steinecker, Denis Trescher, Alexander Bienemann +2End-to-End Autonomous DrivingSim-to-Real Transfer

    Chair of Machine Perception for Autonomous Driving, University of the Bundeswehr Munich, Germany

  25. Comfort by Construction: Adaptive, Comfort-Bounded Action Spaces for Learned Driving Policies

    Sep 11, 2026Anna Rothenhäusler, Daniel Jost, Raghu Rajan +4Constrained RLVehicle Dynamics

    University of Freiburg, Freiburg, Germany · Bosch Center for Artificial Intelligence, Stuttgart, Germany · Coburg University of Applied Sciences, Coburg, Germany

  26. Proactive Context-Forecasted Safety Constraints for Nonstationary Reinforcement Learning

    Sep 8, 2026Tim TomashevskiyNon-Stationary RLConstrained RL

    Department of Computing and Software McMaster University Hamilton, Ontario, Canada

  27. DriveZero: End-to-End Driving Beyond Human Demonstrations

    Sep 5, 2026Hao He, Chengcheng Hu, Zirun Su +17End-to-End Autonomous DrivingRL for Autonomous Driving

    Xiaomi EV AD & Robotics, L3 Team

  28. What Emerges and What Breaks in Self-Play Driving

    Aug 31, 2026Laur Sisask, Ardi Tampuu, Tambet MatiisenReward HackingSelf-Play RL

    University of Tartu, Tartu, Estonia

  29. Aligning Multi-Trajectory Supervision with Policy Optimization for VLA Driving

    Aug 31, 2026Tian Zhang, Zhuo Huang, Hongrui Ye +3VLMs for Autonomous DrivingGroup Relative Policy Optimization

    School of Computer Science, Wuhan University, China · Dongfeng Research & Development Institute

  30. Reinforcement Learning-Based Control of CAV Platoon Joining Maneuvers in Mixed Traffic

    Aug 27, 2026Biao Yin, Abderrahmane Kasmi, Nadir FarhiRL ControlIntelligent Transportation Systems

    Université Gustave Eiffel, SATIE, Gif-sur-Yvette, 91190, France. · Université Paris Dauphine-PSL, Paris, 75016, France. · Cosys-Grettia, Univ Gustave Eiffel, F-77454 Marne-la-Vallee, France.