  Automated parking stands as a highly anticipated application of autonomous
driving technology. However, existing path planning methodologies fall short of
addressing this need due to their incapability to handle the diverse and
complex parking scenarios in reality. While non-learning methods provide
reliable planning results, they are vulnerable to intricate occasions, whereas
learning-based ones are good at exploration but unstable in converging to
feasible solutions. To leverage the strengths of both approaches, we introduce
Hybrid pOlicy Path plannEr (HOPE). This novel solution integrates a
reinforcement learning agent with Reeds-Shepp curves, enabling effective
planning across diverse scenarios. HOPE guides the exploration of the
reinforcement learning agent by applying an action mask mechanism and employs a
transformer to integrate the perceived environmental information with the mask.
To facilitate the training and evaluation of the proposed planner, we propose a
criterion for categorizing the difficulty level of parking scenarios based on
space and obstacle distribution. Experimental results demonstrate that our
approach outperforms typical rule-based algorithms and traditional
reinforcement learning methods, showing higher planning success rates and
generalization across various scenarios. We also conduct real-world experiments
to verify the practicability of HOPE. The code for our solution will be openly
available on \href{GitHub}{https://github.com/jiamiya/HOPE}.
