Recent Advances of Reinforcement Learning Algorithms for Autonomous Driving System

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.