Recent Advances of Reinforcement Learning Algorithms for Autonomous Driving System
TL;DR
This survey examines RL-based AD in modular and end-to-end pipelines and relates reported methods to task formulation and deployment evidence and examines deployment barriers, including safety, Sim2Real generalization, data efficiency, computation, embodied alignment, and evaluation readiness.
Abstract
Reinforcement learning (RL) is being studied for autonomous driving (AD), but its value depends on the role it plays in a task, the action interface, the evaluation protocol, and the evidence from deployment. This survey examines RL-based AD in modular and end-to-end pipelines and relates reported methods to task formulation and deployment evidence. It maps safe RL, offline RL, model-based RL, and PPO/GRPO-style fine-tuning to maneuver selection, continuous control, world modeling, and VLM/VLA-based driving. It also reviews simulators, datasets, RL platforms, and VLA benchmarks, with attention to reward design, observation space, traffic complexity, and open-loop versus closed-loop evaluation. The survey then examines deployment barriers, including safety, Sim2Real generalization, data efficiency, computation, embodied alignment, and evaluation readiness. RL and VLM/VLA-based methods have shown promise, but current evidence is insufficient to support reliable real-world deployment: many reported results come from restricted scenarios and depend on engineered rewards or simulator assumptions.
