The primary objective of this study is to incorporate the deep reinforcement learning (DRL) technique in conflict detection and resolution (CD&R) control strategies to generate an optimised trajectory for air traffic controllers as reference, in order to improve efficiency and reduce the amount of heading angle change. A DRL environment which can be applied to CD&R agent training is developed. The agent receives the current state of multiple aircrafts in a sector and generates an action to change the heading angle of an aircraft to avoid conflict. A K ‐Control Actor‐Critic algorithm is proposed to limit the number of control times and a two‐dimensional continuous action selection policy is utilised. The simulation results show the feasibility of DRL applied in CD&R and there is an obvious advantage in computational efficiency.
Deep reinforcement learning based conflict detection and resolution in air traffic control
IET Intelligent Transport Systems ; 13 , 6 ; 1041-1047
2019-06-01
7 pages
Article (Journal)
Electronic Resource
English
multi‐agent systems , aircraft control , continuous action selection , multiple aircrafts , k‐control actor‐critic algorithm , air traffic control , aircraft heading angle , learning (artificial intelligence) , trajectory optimisation (aerospace) , deep reinforcement learning , conflict detection and resolution control , CD&R control , optimised trajectory
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