基于深度强化学习的机场货运业务优化研究

为了将智能Agent技术架构应用于机场货运业务的仿真模型开发,以机场货运资源优化为目标,提出了将深度强化学习与机场货运业务仿真模型结合的决策支持系统框架,用仿真数据实现对深度学习网络的训练,运用深度学习网络优化模型中的调度方案.训练成熟的系统采取在线模式,可以用于实时优化货运流程的调度方案.为了验证架构的有效性,在An...

Research on Optimization of Airport Cargo Business Based on Deep Reinforcement Learning

An intelligent agent technology architecture is adopted to the simulation model development of airport cargo business. Aiming at the optimization of airport cargo resources, a decision support system framework combining deep reinforcement learning (DRL) and airport cargo business simulation model is proposed. The simulated results are applied as the training data of the DRL network, and the DRL is used to optimize operation parameter of the simulation model. The mature system can be run online, which can provide optimized operation order in real time. In order to verify the effectiveness of the architecture, model development and experiments are conducted in Anylogic simulation platform, and the performances of DRL and OptQuest are compared. The results show that DRL can better optimize airport cargo business on the basis of ensuring orderly airport cargo operations.