Deep reinforcement learning (DRL) has been successfully adopted in many tasks, such as autonomous driving and gaming, to achieve or surpass human-level performance. This paper proposes a DRL-based trajectory planner for automated parking systems (APS). A thorough review of literature in this field is presented. A simulation study is conducted to investigate the trajectory planning performance of the parking agent for: (i) different neural-network architectures; (ii) different training set-ups; (iii) efficacy of human-demonstration. Real-time capability of the proposed planner on various embedded hardware platforms is also discussed by the paper, showing promising performance. Insights of the use of DRL for APS are concluded at the end of the paper.

中文翻译：

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使用深度强化学习的自动泊车系统的轨迹规划

深度强化学习（DRL）已成功用于许多任务中，例如自动驾驶和游戏，以达到或超越人类水平的表现。本文提出了一种基于DRL的自动泊车系统（APS）的轨迹规划器。介绍了对该领域文献的全面回顾。进行了仿真研究以调查驻车剂在以下方面的轨迹规划性能：（i）不同的神经网络架构；（ii）不同的训练方式；（iii）人体示范的功效。本文还讨论了拟议的计划程序在各种嵌入式硬件平台上的实时功能，显示了令人鼓舞的性能。本文结尾部分总结了将DRL用于APS的见解。