Flight conflict resolution method based on relative entropy inverse reinforcement learning
The primary objective of air traffic management is to ensure the safety of aircraft flights.Flight conflicts can lead to hazardous approaches or even collisions,resulting in severe consequences.Therefore,studying auxiliary tools to assist controllers in resolving flight conflicts becomes essential.This article aims to enhance the personalization level of regulatory decision-making tools and improve controllers'acceptance of conflict resolution solutions provided by these tools.Firstly,this article adopts an inverse reinforcement learning method based on relative entropy to extract implicit controller instruction strategies from aircraft flight trajectory data and represent them as reward functions.The flight conflict resolution problem is then modeled using the Markov decision process,and the deep reinforcement learning method(DQN algorithm)is employed to train the model guided by the aforementioned reward function.The objective is to enhance the success rate of the resolution models and the degree of strategy personalization.Additionally,the article introduces analysis indicators from two perspectives:safety and applicability.Finally,a simulation system based on the Base of Aircraft Data(BADA)database is utilized to generate 5 000 flight conflict scenarios.Out of these,4 000 scenarios are used for model training,and the remaining 1 000 are employed to verify the effectiveness of the proposed method.Experimental results demonstrate that,under the guidance of a reward function incorporating controller strategies,the resolution model consistently improves the success rate of flight conflict scenarios and the similarity to controller strategies.During the testing phase,the successful resolution rate exceeds 70％.This result validates that the inverse reinforcement learning method based on relative entropy effectively learns the empirical knowledge of controllers,thereby enhancing the efficiency and personalization level of the resolution models.These methods present a novel approach to studying and improving the level of personification in control schemes,which has practical significance in enhancing the efficiency of air traffic operations and ensuring airspace safety.
NETL
NSTL
万方数据
维普
