<ul><li>Reinforcement learning (RL) has emerged as a promising approach for motion planning challenges in autonomous driving (AD).</li><li>This survey provides a comprehensive review of RL-based motion planning, focusing on lessons learned from a driving task perspective.</li><li>It outlines the fundamentals of RL methodologies and analyzes their applications in motion planning, considering scenario-specific features and task requirements.</li><li>The survey also identifies frontier challenges and proposes strategies for overcoming unresolved issues in RL-based motion planning.</li></ul>

A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective

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