多智能体强化学习在直升机机场调度中的应用

Multi-Agent Reinforcement Learning in Helicopter Airport Dispatching

快速高效的直升机机场调度是现代直升机机场调度系统面临的主要挑战。设计了一个直升机机场调度试验平台,使用二维网格环境,供多种算法进行快速试验。机场调度试验平台根据机场实际地形进行地图编辑,提供了传统的集中式规划算法和基于多智能体强化学习算法来进行快速高效的模拟调度实验。实验表明,基于多智能体强化学习方法的可扩展性和实时规划效果较好。试验平台为进一步研究机场调度提供了良好的起点,对未来多智能体路径规划问题应用于实际场景将会产生有益影响。

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.