RL for Autonomous Driving

RL: Reinforcement Learning

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10 papers in the last four weeks, up 150% on the four weeks before. 0.1% of all new papers.

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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 Learning RL 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
Vision-Language-Action Autonomous Driving Agent with Language-based Memory
Sep 29, 2026Kai Yan, Xiangyu Chen, Yulong Cao +9Memory-Augmented VLMs VLMs for Autonomous Driving
University of Illinois Urbana-Champaign · NVIDIA
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 RL RL 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
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 Driving Action-Conditioned World Models
The Chinese University of Hong Kong, Shenzhen · XPeng Motors · Xidian University
RefineDrive: Reliable Failure-Guided Learning for Vision-Language-Action Driving
Sep 28, 2026Zhe Sun, Ziyi Luo, Yehao Lu +2VLMs for Autonomous Driving Autonomous Driving Planning
College of Computer Science and Technology, Zhejiang University, Hangzhou, China · Yinwang Intelligent Technology Co., Ltd.
Vision--Language Signals in Constrained RL: Safety Gains Without Anticipation
Sep 28, 2026Samuel Tetteh, Cody FlemingConstrained RL RL for Autonomous Driving
Iowa State University Ames, Iowa, USA
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 Driving Group Relative Policy Optimization
Peking University · Minieye Technology Co., Ltd · Fuzhou University
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 Driving Sim-to-Real Transfer
Chair of Machine Perception for Autonomous Driving, University of the Bundeswehr Munich, Germany
Comfort by Construction: Adaptive, Comfort-Bounded Action Spaces for Learned Driving Policies
Sep 14, 2026Anna Rothenhäusler, Daniel Jost, Raghu Rajan +4Constrained RL Vehicle Dynamics
University of Freiburg, Freiburg, Germany · Bosch Center for Artificial Intelligence, Stuttgart, Germany · Coburg University of Applied Sciences, Coburg, Germany
Proactive Context-Forecasted Safety Constraints for Nonstationary Reinforcement Learning
Sep 8, 2026Tim TomashevskiyNon-Stationary RL Constrained RL
Department of Computing and Software McMaster University Hamilton, Ontario, Canada
What Emerges and What Breaks in Self-Play Driving
Aug 31, 2026Laur Sisask, Ardi Tampuu, Tambet MatiisenReward Hacking Self-Play RL
University of Tartu, Tartu, Estonia
Aligning Multi-Trajectory Supervision with Policy Optimization for VLA Driving
Aug 31, 2026Tian Zhang, Zhuo Huang, Hongrui Ye +3VLMs for Autonomous Driving Group Relative Policy Optimization
School of Computer Science, Wuhan University, China · Dongfeng Research & Development Institute
Language-Structured Relational Q-Learning for Threat-Aware Control in Safety-Critical Driving
Aug 11, 2026Aditya Humnabadkar, Huaizhong Zhang, Ardhendu BeheraReinforcement Learning Autonomous Driving Safety Evaluation
Department of Computer Sciences, Edge Hill University, United Kingdom
Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving
Aug 11, 2026Jiazhuo Li, Linjiang Cao, Qi Liu +1World Models for Autonomous Driving RL for Autonomous Driving
Tongji University
Pictura: Perspective-View Self-Play at Scale for Driving
Jul 28, 2026Yuan Yin, Elias Ramzi, Marc Lafon +8Self-Play RL RL for Autonomous Driving
valeo.ai, Paris, France · Valeo Brain, Créteil, France · Sorbonne Université, CNRS, ISIR, F-75005 Paris, France
Optimal Reward Shaping: Autonomous Car Parking Case Study
Jul 26, 2026Emre Özkaya, Nicolas R. GaugerDeep Q-Learning Autonomous Parking
Chair for Scientific Computing, RPTU University of Kaiserslautern-Landau Paul-Ehrlich-Strasse 34, 67663 Kaiserslautern
Structured Reinforcement Learning for Bayesian Persuasion : Application to Intelligent Interactive Driving
Jul 15, 2026Merlin Paul, Anup ApremReinforcement Learning RL for Autonomous Driving
TerraZero: Procedural Driving Simulation for Zero-Demonstration Self-Play at Scale
Jul 14, 2026Zhouchonghao Wu, Akshay Rangesh, Weixin Li +4Self-Play RL RL for Autonomous Driving
1Applied Intuition · University of California, Berkeley
LIDAR-AD: A Decoder-Free Latent-Interaction Dreamer with Action-Residual Chains for Autonomous Driving
Jul 12, 2026Yongzhi Liu, Yang Xiao, Zhong Cao +5Latent Dynamics Modeling World Models for Autonomous Driving
School of Mechanical Engineering, Southeast University, Nanjing, Jiangsu 211189, China · Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI 48109, USA · Xheart Technology Co., Ltd., Suzhou, Jiangsu 215100, China
A Risk-Field Enhanced Closed-Loop Digital Twin Framework for Autonomous Driving Safety Validation
Jul 7, 2026Yongzhi LiuAutonomous Driving Safety Evaluation Digital Twins
School of Mechanical Engineering Southeast University Nanjing, China
CRRL: A Causality-Based Reinforcement Learning Framework for Autonomous System Recovery
Jul 3, 2026Safia Fatima, Kai Olav Ellefsen, Leon MoonenReinforcement Learning Robot Failure Recovery
Simula Research Laboratory, Norway · University of Oslo, Norway
CLEAR: Closed-Loop Reinforcement Learning at Scale for End-to-End Autonomous Driving
Jul 3, 2026Yunxiao Shi, Hong Cai, Mohammad Ghavamzadeh +1VLMs for Autonomous Driving End-to-End Autonomous Driving
Qualcomm AI Research
What Probing Reveals about Autonomous Driving: Linking Internal Prediction Errors to Ego Planning
Jun 30, 2026Hyeonchang Jeon, Kyungbeom Kim, Eugene Vinitsky +1Autonomous Driving Safety Evaluation Autonomous Driving Planning
1Gwangju Institute of Science and Technology (GIST) · 2New York University
PlanRL: A Trajectory Planning Architecture for Reinforcement Learning-based Driving Experts
Jun 25, 2026Joonhee Lim, Yongjae Lee, Jangho Shin +1Motion Planning Autonomous Driving Planning
Robotics Program, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, South Korea · Cho Chun Shik Graduate School of Mobility, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, South Korea · Hyundai Motor Company, Hwaseong 18280, South Korea
Sample-efficient Transfer Reinforcement Learning via Adaptive Reward Shaping and Policy-Ratio Reweighting Strategy
Jun 25, 2026Wenjie Huang, Yang Li, Jingjia Teng +6Reinforcement Learning Reward Shaping
State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China · Key Laboratory of Intelligent Air-Ground Cooperative Control for Universities in Chongqing, School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China · Xi’an Institute of High-Tech, Xi’an 710025, China +1
Reward-Conditioned Attention: How Reward Design Shapes What Autonomous Driving Agents See
Jun 23, 2026Mohamed Benabdelouahad, Ahmed Djalal Hacini, Nadir Farhi +1Reward Shaping RL for Autonomous Driving
National School of Artificial Intelligence (ENSIA), Sidi Abdellah Campus, Algiers, Algeria. · Cosys-Grettia, Univ Gustave Eiffel, F-77454 Marne-la-Vallee, France.
FAST: A Framework for Aligned Sampling and Training in Parallel Reinforcement Learning for Autonomous Driving
Jun 19, 2026Bonan Wang, Letian Tao, Bin Shuai +7Reinforcement Learning RL for Autonomous Driving
School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China · Didi Voyager Labs, DiDi Autonomous Driving · School of Vehicle and Mobility and the College of AI, Tsinghua University, Beijing 100084, China
TerraTransfer: Learning End-to-End Driving Policies Without Expert Demonstrations
Jun 16, 2026Zikang Xiong, Weixin Li, Zhouchonghao Wu +6End-to-End Autonomous Driving RL for Autonomous Driving
1Applied Intuition · 2UCLA · 3UC Berkeley
ROSA-RL: Uncertainty-Aware Roundabout Optimized Speed Advisory with Reinforcement Learning
Jun 15, 2026Anna-Lena Schlamp, Jeremias Gerner, Klaus Bogenberger +2Autonomous Driving Planning RL for Autonomous Driving
Institute AImotion Bavaria Technische Hochschule Ingolstadt Ingolstadt, Germany · School of Engineering and Design Technical University of Munich Munich, Germany · CARISSMA Institute of Automated Driving (C-IAD) Technische Hochschule Ingolstadt Ingolstadt, Germany
Hamilton-Jacobi Reachability-Based Safe Reinforcement Learning for Emergency Collision Avoidance
Jun 13, 2026Yuhong Jiang, Shiyue Zhao, Junzhi Zhang +4Collision Avoidance Reachability Analysis
School of Vehicle and Mobility, Tsinghua University, Beijing, China · State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing, China

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

Huiwen Yan, Kyriakos G. Vamvoudakis, Mushuang Liu

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

Meta-Reinforcement Learning RL for Autonomous Driving Multi-Agent Reinforcement Learning

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This paper develops a meta-multi-agent reinforcement learning (meta-MARL) framework to enable fast adaptation of interactive policies in a multi-agent system (MAS). Meta-reinforcement learning (meta-RL) enables agents to rapidly adapt to new tasks/environments using a bi-level optimization mechanism. However, existing meta-RL generally focuses on single-agent systems. Extending these frameworks and algorithms to multi-agent systems poses additional challenges, as tasks are characterized by not only the environment but also agents' strategic interactions. To address these challenges, we model multi-agent reinforcement learning (MARL) problems as Markov games (MGs) and develop a meta-MARL framework for rapid interactive policy adaptation across a distribution of MGs. A new concept, called meta-NE, is defined to describe the desired solution concept in a meta-MARL problem. Sufficient conditions for the equivalence between a meta-NE and a stationary point of the gradient-play-based meta-MARL algorithm are established. Our evaluation on autonomous-driving tasks demonstrates that the proposed meta-MARL method achieves faster adaptation than pretrained MARL baselines, validating the effectiveness of our framework.

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