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- W2910515609 abstract "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." @default.
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- W2910515609 date "2019-02-21" @default.
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- W2910515609 title "Deep reinforcement learning based conflict detection and resolution in air traffic control" @default.
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- W2910515609 doi "https://doi.org/10.1049/iet-its.2018.5357" @default.
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