论文标题
共享自动驾驶汽车（SAV）车队管理的深入加强学习
Deep Reinforcement Learning for Shared Autonomous Vehicles (SAV) Fleet Management
论文作者
论文摘要
共享的自动化车辆（SAV）车队公司正在全国范围内开设试点项目。 2020年，在弗吉尼亚州费尔法克斯（Fairfax Virginia），宣布了第一个在弗吉尼亚州共享的自动驾驶汽车舰队飞行员项目。 SAVS承诺可以改善生活质量。但是，SAV还将通过产生过多的车辆行驶（VMT）来引起一些负面的外部性，从而导致更多的拥塞，能源消耗和排放。过多的VMT主要是通过空的重定位过程生成的。正在研究基于增强学习的算法是解决其中一些问题的可能解决方案：最著名的是将骑手的等待时间降至最低。但是，尚未对降低停车位成本或减少空空巡航时间进行增强学习的研究。这项研究探讨了不同的\ textbf {加固学习方法，然后决定最大程度地减少骑手等待时间，停车成本和空旅旅行的最佳方法
Shared Automated Vehicles (SAVs) Fleets companies are starting pilot projects nationwide. In 2020 in Fairfax Virginia it was announced the first Shared Autonomous Vehicle Fleet pilot project in Virginia. SAVs promise to improve quality of life. However, SAVs will also induce some negative externalities by generating excessive vehicle miles traveled (VMT), which leads to more congestions, energy consumption, and emissions. The excessive VMT are primarily generated via empty relocation process. Reinforcement Learning based algorithms are being researched as a possible solution to solve some of these problems: most notably minimizing waiting time for riders. But no research using Reinforcement Learning has been made about reducing parking space cost nor reducing empty cruising time. This study explores different \textbf{Reinforcement Learning approaches and then decide the best approach to help minimize the rider waiting time, parking cost, and empty travel
