Deep-Dispatch: A Deep Reinforcement Learning-Based Vehicle Dispatch Algorithm for Advanced Air Mobility

2023-12-17 · Elaheh Sabziyan Varnousfaderani, Syed A. M. Shihab, Esrat F. Dulia

Near future air taxi operations with electric vertical take-off and landing (eVTOL) aircraft will be constrained by the need for frequent recharging of eVTOLs, limited takeoff and landing pads in vertiports, and subject to time-varying demand and electricity prices, making the eVTOL dispatch problem unique and particularly challenging to solve. Previously, we 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, we have developed two deep reinforcement learning-based eVTOL dispatch algorithms, namely single-agent and multi-agent deep Q-learning eVTOL dispatch algorithms, where the objective is to maximize operating profit. An eVTOL-based passenger transportation simulation environment was built to assess the performance of our algorithms across $36$ numerical cases with varying number of eVTOLs, vertiports, and demand. The results indicate that the multi-agent eVTOL 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.

📄 PDF Abstract BibTeX arXiv:2312.10809

Code (0)

등록된 구현이 없습니다.

Tasks

Deep Reinforcement LearningQ-Learning

Methods 이 논문이 사용한 방법론

Q-Learning Q-Learning is an off-policy temporal difference control algorithm: $$Q\left(S\_{t}, A\_{t}\right) \leftarrow Q\left(S\_{t}, A\_{t}\right) + \alpha\left[R_{t+1} +…

Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matching

2019-10-07 · Ming Zhou, Jiarui Jin, Wei-Nan Zhang, Zhiwei Qin 외

Improving the efficiency of dispatching orders to vehicles is a research hotspot in online ride-hailing systems. Most of the existing solutions for order-dispatching are centralized controlling, which require to consider…

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

GARLIC: GPT-Augmented Reinforcement Learning with Intelligent Control for Vehicle Dispatching

2024-08-19 · Xiao Han, Zijian Zhang, Xiangyu Zhao, Yuanshao Zhu 외

As urban residents demand higher travel quality, vehicle dispatch has become a critical component of online ride-hailing services. However, current vehicle dispatch systems struggle to navigate the complexities of urban …

Navigate reinforcement-learning Reinforcement Learning

A Modular and Transferable Reinforcement Learning Framework for the Fleet Rebalancing Problem

2021-05-27 · Erotokritos Skordilis, Yi Hou, Charles Tripp, Matthew Moniot 외

Mobility on demand (MoD) systems show great promise in realizing flexible and efficient urban transportation. However, significant technical challenges arise from operational decision making associated with MoD vehicle d…

Decision Making reinforcement-learning Reinforcement Learning (RL)Transfer Learning

A SUMO Framework for Deep Reinforcement Learning Experiments Solving Electric Vehicle Charging Dispatching Problem

2022-09-07 · Yaofeng Song, Han Zhao, Ruikang Luo, Liping Huang 외

In modern cities, the number of Electric vehicles (EV) is increasing rapidly for their low emission and better dynamic performance, leading to increasing demand for EV charging. However, due to the limited number of EV c…

Deep Reinforcement Learning Reinforcement Learning (RL)

Atomic Proximal Policy Optimization for Electric Robo-Taxi Dispatch and Charger Allocation

2025-02-19 · Jim Dai, Manxi Wu, Zhanhao Zhang

Pioneering companies such as Waymo have deployed robo-taxi services in several U.S. cities. These robo-taxis are electric vehicles, and their operations require the joint optimization of ride matching, vehicle reposition…

Deep Reinforcement Learning Scheduling