论文标题
安排使用安全学习的城市空气流动性
Scheduling for Urban Air Mobility using Safe Learning
论文作者
论文摘要
这项工作考虑了城市空气流动性（UAM）车辆的调度问题，并在起源与艰难和软旅行期限之间均可进行。 每条路线都通过在旅行完成时间（或延迟）中的离散概率分布以及固定的硬或软截止日期以及固定的硬性或软截止日期以及固定的请求（或需求）的交流时间（或需求）来描述。 软截止日期的截止日期遗漏时会产生的费用。 开发了一个在线安全的调度程序，可确保艰难的截止日期永远不会错过，并且丢失软截止日期的平均成本被最小化。 该系统被建模为马尔可夫决策过程（MDP），并使用基于模型的安全学习来查找有关路线延迟和需求的概率分布。 蒙特卡洛树搜索（MCTS）最早的截止日期（EDF）用于以在线方式安全地探索学识渊博的模型，并制定近乎最理想的非首选调度策略。 将这些结果与价值迭代（VI）和MCT（随机）调度解决方案进行比较。
This work considers the scheduling problem for Urban Air Mobility (UAM) vehicles travelling between origin-destination pairs with both hard and soft trip deadlines. Each route is described by a discrete probability distribution over trip completion times (or delay) and over inter-arrival times of requests (or demand) for the route along with a fixed hard or soft deadline. Soft deadlines carry a cost that is incurred when the deadline is missed. An online, safe scheduler is developed that ensures that hard deadlines are never missed, and that average cost of missing soft deadlines is minimized. The system is modelled as a Markov Decision Process (MDP) and safe model-based learning is used to find the probabilistic distributions over route delays and demand. Monte Carlo Tree Search (MCTS) Earliest Deadline First (EDF) is used to safely explore the learned models in an online fashion and develop a near-optimal non-preemptive scheduling policy. These results are compared with Value Iteration (VI) and MCTS (Random) scheduling solutions.
