Open AccessDeep reinforcement learning based conflict detection and resolution in air traffic control
Author(s) -
Wang Zhuang,
Li Hui,
Wang Junfeng,
Shen Feng
Publication year - 2019
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2018.5357
Subject(s) - reinforcement learning , heading (navigation) , conflict resolution , air traffic control , trajectory , computer science , control (management) , artificial intelligence , limit (mathematics) , action (physics) , control theory (sociology) , engineering , real time computing , mathematics , aerospace engineering , law , political science , mathematical analysis , physics , quantum mechanics , astronomy
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.