DeepDispatch: Deep Reinforcement Learning-Based Vehicle Dispatch Algorithm for Advanced Air Mobility
强化学习
计算机科学
人工智能
汽车工程
算法
工程类
作者
Elaheh Sabziyan Varnousfaderani,Syed Arbab Mohd Shihab,Esrat F. Dulia
出处
期刊:Journal of air transportation [American Institute of Aeronautics and Astronautics] 日期:2024-06-10卷期号:: 1-22被引量:3
标识
DOI:10.2514/1.d0416
摘要
Near-future air taxi operations with electric vertical takeoff and landing aircraft will be constrained by the need for frequent recharging and limited takeoff and landing pads in vertiports and will be subject to time-varying demand and electricity prices, making the dispatch problem unique and particularly challenging to solve. Previously, the authors have developed optimization models to address this problem. Such optimization models, however, suffer from prohibitively high computational run times when the scale of the problem increases, making them less practical for real-world implementation. To overcome this issue, the authors have developed two deep reinforcement learning-based dispatch algorithms, namely, single-agent and multi-agent double dueling deep Q-network dispatch algorithms, where the objective is to maximize operating profit. A passenger transportation simulation environment was built to assess the performance of these algorithms across 36 numerical cases with varying numbers of vehicles and vertiports and amounts of demand. The results indicate that the multi-agent dispatch algorithm can closely approximate the optimal dispatch policy with significantly less computational expenses compared to the benchmark optimization model. The multi-agent algorithm was found to outperform the single-agent counterpart with respect to both profits generated and training time. Additionally, we implemented a heuristic-based algorithm, faster but less effective in generating profits compared to our two deep reinforcement learning-based algorithms.