Abstract: With the increase in military,civilian,and general aviation flight activities,the conflict over airspace use has become prominent,and it has become a normal phenomenon for multiple aircraft to fly simultaneously in the same airspace.Therefore,it is an urgent problem that needs to be solved how to provide assistance in avoiding flight collisions through technical means.To tackle the challenge of resolving conflicts between multiple aircraft in flight,this paper introduces a Graph Convolutional Deep Reinforcement Learning(GDQN) algorithm.This algorithm combins multi-agent deep reinforcement learning with a graph con-volutional neural network framework.Initially,it constructs a message-passing function to develop a multi-agent flight conflict model,which can navigate multiple aircraft through three-dimensional,unstructures airspace while avoiding conflicts and collisions.Subsequently,it employes a deep self-learning method based on graph convolutional networks to offer intelligent conflict avoidance solutions for airport scheduling,creats a multi-agent system(MAS) for managing multi-aircraft conflict scenarios.The effectiveness of the algorithm is validated through simulations using extensive training datasets in a controlled environment.The results indicate that the optimized algorithm is effective,achieving a conflict resolution success rate of over 90%,with resolution decision times of less than 3 seconds.Additionally,it significantly reduces the number of air traffic control(ATC) commands issued and improves overall operational efficiency.

Key words: Deep reinforcement learning, Graph convolutional neural network, Message passing, Multi-agent model, Multi-aircraft flight, Conflict resolution