Multi-Agent Reinforcement Learning in Helicopter Airport Dispatching

被引：0

作者：

Liu, Zhifei ^{[1
]}

Dong, Qiang ^{[1
]}

Lai, Jun ^{[1
]}

Chen, Xiliang ^{[1
]}

机构：

[1] College of Command and Control Engineering, Army Engineering University, Nanjing

来源：

Computer Engineering and Applications | 2023年 / 59卷 / 16期

关键词：

airport dispatching; multi-agent path finding; reinforcement learning; test platform;

D O I：

10.3778/j.issn.1002-8331.2205-0370

中图分类号：

学科分类号：

摘要：

Fast and efficient helicopter airport dispatching is the main challenge faced by modern helicopter airport dispatching system. Helicopter airport dispatching can be regarded as a classical multi-agent path finding problem. A helicopter airport dispatching test platform is designed, which uses a two-dimensional grid environment for rapid test of various algorithms. The airport dispatching test platform edits the map according to the actual terrain of the airport, and provides the traditional centralized planning algorithm and the algorithm based on multi-agent reinforcement learning to carry out fast and efficient simulation dispatching experiments. In order to explore the potential of multi-agent reinforcement learning in airport scheduling, a large number of experiments are carried out, and the applicability and characteristics of different types of algorithms are compared and analyzed. The experimental results show that the reinforcement learning method based on multi-agent has good scalability and real-time planning effect. The test platform provides a good starting point for further research on airport scheduling, and will have a beneficial impact on the application of multi-agent path finding in practical scenarios in the future. © 2023 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.

引用

页码：285 / 294

页数：9

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