# HOPE: A Reinforcement Learning-based Hybrid Policy Path Planner for Diverse Parking Scenarios

arXiv: https://arxiv.org/abs/2405.20579 · HTML: https://mlsanity.com/paper/2405.20579/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods.
- Builds on 15 of its 43 references that are in the map.
- First posted on arXiv 31 May 2024.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer](https://mlsanity.com/paper/2207.14024/) (2022)
- [A Closer Look at Invalid Action Masking in Policy Gradient Algorithms](https://mlsanity.com/paper/2006.14171/) (2020)
- [Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives](https://mlsanity.com/paper/2303.09824/) (2023)
- [Optimization-Based Collision Avoidance](https://mlsanity.com/paper/1711.03449/) (2017)
- [ParkPredict+: Multimodal Intent and Motion Prediction for Vehicles in Parking Lots with CNN and Transformer](https://mlsanity.com/paper/2204.10777/) (2022)
- [An Efficient Spatial-Temporal Trajectory Planner for Autonomous Vehicles in Unstructured Environments](https://mlsanity.com/paper/2208.13160/) (2022)
- [Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning](https://mlsanity.com/paper/2102.03127/) (2021)
- [Tactics2D: A Highly Modular and Extensible Simulator for Driving Decision-making](https://mlsanity.com/paper/2311.11058/) (2023)
- [TDR-OBCA: A Reliable Planner for Autonomous Driving in Free-Space Environment](https://mlsanity.com/paper/2009.11345/) (2020)

## Built on by (most cited first, 11 of 11)

- [RL-OGM-Parking: Lidar OGM-Based Hybrid Reinforcement Learning Planner for Autonomous Parking](https://mlsanity.com/paper/2502.18846/) (2025)
- [From Imitation to Exploration: End-to-end Autonomous Driving based on World Model](https://mlsanity.com/paper/2410.02253/) (2024)
- [Embodied Escaping: End-to-End Reinforcement Learning for Robot Navigation in Narrow Environment](https://mlsanity.com/paper/2503.03208/) (2025)
- [SEG-Parking: Towards Safe, Efficient, and Generalizable Autonomous Parking via End-to-End Offline Reinforcement Learning](https://mlsanity.com/paper/2509.13956/) (2025)
- [A Diffusion-Refined Planner with Reinforcement Learning Priors for Confined-Space Parking](https://mlsanity.com/paper/2510.14000/) (2025)
- [Adapting Reinforcement Learning for Path Planning in Constrained Parking Scenarios](https://mlsanity.com/paper/2601.22545/) (2026)
- [REAP: Reinforcement-Learning End-to-End Autonomous Parking with Gaussian Splatting Simulator for Real2Sim2Real Transfer](https://mlsanity.com/paper/2605.08713/) (2026)
- [ParkingWorld: End-to-End Autonomous Parking Reinforcement Learning from Corrective Experience in 3DGS Simulation](https://mlsanity.com/paper/2605.25029/) (2026)
- [NeuralParker: A Reinforcement Learning Planner for Irregular Parking Environments](https://mlsanity.com/paper/2608.24485/) (2026)
- [Learning Reliable Parking Policies via Offline Reinforcement Learning with Quantized Action Representations](https://mlsanity.com/paper/2609.19894/) (2026)
- [Timed Rule-Based Supervision of an End-to-End Autonomous Parking Policy](https://mlsanity.com/paper/2609.31773/) (2026)

## Abstract (arXiv)

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 is openly available on https://github.com/jiamiya/HOPE.