Abstract:

In order to enhance the continual effectiveness of intelligent flight conflict resolution methods, considering the dynamic changes of factors such as airspace environment, a continual learning mechanism into the intelligent flight conflict resolution algorithm is proposed. Firstly, the flight conflict resolution problem based on Markov decision process is modeling. And then, the model using deep reinforcement learning methods is trained to effectively resolve flight conflicts. Finally, two continual learning methods, namely parameter isolation and meta-learning, are introduced to facilitate the model to rapidly adapt to new conflict scenarios. The experiment results show that after introducing the continual learning methods, the model achieves an initial successful rate of nearly over 70% in conflict resolution, eventually over 90%. The model retains over 87% of its memory for the initial training scenarios, effectively avoiding catastrophic forgetting and improving its continual learning capability. The model is of great significance in ensuring flight safety, reducing the workload of air traffic controllers and improving the efficiency of air traffic operations.

Key words: air traffic control, flight conflict resolution, continual learning, deep reinforcement learning