Reinforcement learning (RL), with its ability to explore and optimize policies in complex, dynamic decision-making tasks, has emerged as a promising approach to addressing motion planning (MoP) challenges in autonomous driving (AD). Despite rapid advancements in RL and AD, a systematic description and interpretation of the RL design process tailored to diverse driving tasks remains underdeveloped. This survey provides a comprehensive review of RL-based MoP for AD, focusing on lessons from task-specific perspectives. We first outline the fundamentals of RL methodologies, and then survey their applications in MoP, analyzing scenario-specific features and task requirements to shed light on their influence on RL design choices. Building on this analysis, we summarize key design experiences, extract insights from various driving task applications, and provide guidance for future implementations. Additionally, we examine the frontier challenges in RL-based MoP, review recent efforts to addresse these challenges, and propose strategies for overcoming unresolved issues.

翻译：强化学习（RL）凭借其在复杂动态决策任务中探索与优化策略的能力，已成为应对自动驾驶（AD）中运动规划（MoP）挑战的一种前景广阔的方法。尽管强化学习与自动驾驶领域进展迅速，但针对多样化驾驶任务的强化学习设计流程的系统性描述与阐释仍显不足。本综述对基于强化学习的自动驾驶运动规划进行了全面回顾，重点从任务特定视角总结实践经验。我们首先概述强化学习方法的基本原理，继而综述其在运动规划中的应用，通过分析场景特异性特征与任务需求，阐明这些因素对强化学习设计选择的影响。基于此分析，我们总结了关键设计经验，从各类驾驶任务应用中提炼见解，并为未来实践提供指导。此外，我们探讨了基于强化学习的运动规划面临的前沿挑战，回顾了近期应对这些挑战的研究进展，并就尚未解决的问题提出了应对策略。