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Reinforcement Learning Methods for Autonomous Driving: A Survey
- 1 Department of Computer Science and Technology, Beijing Jiaotong University, Beijing, China
* Author to whom correspondence should be addressed.
Abstract
In recent years, with the rapid development of intelligent transportation, Reinforcement Learning (RL), as an adaptive decision-making method, has gradually permeated into various levels of Autonomous Driving (AD). Therefore, this paper reviews the latest advances in the application of RL in AD. In terms of high-level decision-making and behavioral planning, RL, combined with visual-language models, imitation learning, multi-stage training, and autoregressive trajectory planning, systematically improves planning accuracy and task success rates. At the motion control level, the synergistic optimization of deep reinforcement learning (DRL) based continuous control strategies and robust control methods enhances performance in path tracking, dynamic obstacle avoidance, and multi-sensor information fusion. Meanwhile, end-to-end autonomous driving leverages novel frameworks such as closed-loop RL, World Model (WM), and multimodal decision fusion, effectively narrowing the gap between simulation and real-world environments while achieving significant improvements in safety and smoothness. Additionally, the paper discusses the limitations of RL applications, including data dependency, training efficiency, safety, and interpretability. Furthermore, it explores the future prospects for achieving more intelligent autonomous driving systems through strategies such as meta-learning, transfer learning, adversarial training, and human-machine collaboration.
Keywords
Reinforcement Learning (RL), Autonomous Driving (AD), high-level decision-making, motion control, end-to-end
[1]. Jiang, B., Chen, S., Zhang, Q., Liu, W., & Wang, X. (2025). AlphaDrive: Unleashing the Power of VLMs in Autonomous Driving via Reinforcement Learning and Reasoning. arXiv preprint arXiv:2503.07608.
[2]. Pang, H., Wang, Z., & Li, G. (2024). Large Language Model guided Deep Reinforcement Learning for Decision Making in Autonomous Driving. arXiv preprint arXiv:2412.18511.
[3]. He, B., & Li, Y. (2024). MRIC: Model-Based Reinforcement-Imitation Learning with Mixture-of-Codebooks for Autonomous Driving Simulation. arXiv preprint arXiv:2404.18464.
[4]. Zhang, D., Liang, J., Guo, K., Lu, S., Wang, Q., Xiong, R., ... & Wang, Y. (2025). CarPlanner: Consistent Auto-regressive Trajectory Planning for Large-scale Reinforcement Learning in Autonomous Driving. arXiv preprint arXiv:2502.19908.
[5]. Garg, A., & Krishna, K. M. (2024). Imagine-2-Drive: High-Fidelity World Modeling in CARLA for Autonomous Vehicles. arXiv preprint arXiv:2411.10171.
[6]. Lelkó, A., Németh, B., Fényes, D., & Gáspár, P. (2023). Integration of robust control with reinforcement learning for safe autonomous vehicle motion. IFAC-PapersOnLine, 56(2), 1101-1106.
[7]. Du, G., Zou, Y., Zhang, X., & Zhao, K. (2024, June). Motion Control of Autonomous Vehicle with Domain-Centralized Electronic and Electrical Architecture based on Predictive Reinforcement Learning Control Method. In 2024 IEEE Intelligent Vehicles Symposium (IV) (pp. 1409-1416). IEEE.
[8]. Pérez-Gil, Ó., Barea, R., López-Guillén, E., Bergasa, L. M., Gómez-Huélamo, C., Gutiérrez, R., & Díaz-Díaz, A. (2022). Deep reinforcement learning based control for Autonomous Vehicles in CARLA. Multimedia Tools and Applications, 81(3), 3553-3576.
[9]. Gao, H., Chen, S., Jiang, B., Liao, B., Shi, Y., Guo, X., ... & Wang, X. (2025). Rad: Training an end-to-end driving policy via large-scale 3dgs-based reinforcement learning. arXiv preprint arXiv:2502.13144.
[10]. Gao, D., Cai, S., Zhou, H., Wang, H., Soltani, I., & Zhang, J. (2024). Cardreamer: Open-source learning platform for world model based autonomous driving. IEEE Internet of Things Journal.
[11]. Li, Y., Jiang, M., Zhang, S., Yuan, W., Wang, C., & Yang, M. (2024). End-to-end Driving in High-Interaction Traffic Scenarios with Reinforcement Learning. arXiv preprint arXiv:2410.02253.
[12]. Huang, Z., Sun, S., Zhao, J., & Mao, L. (2023). Multi-modal policy fusion for end-to-end autonomous driving. Information Fusion, 98, 101834.
Cite this article
Lin,Y. (2025). Reinforcement Learning Methods for Autonomous Driving: A Survey. Applied and Computational Engineering,158,41-48.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of CONF-SEML 2025 Symposium: Machine Learning Theory and Applications
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