Review of Deep Reinforcement Learning Approaches for Conflict Resolution in Air Traffic Control
Deep reinforcement learning (DRL) has been widely adopted recently for its ability to solve decision-making problems that were previously out of reach due to a combination of nonlinear and high dimensionality. In the last few years, it has spread in the field of air traffic control (ATC), particular...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-05-01
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| Series: | Aerospace |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2226-4310/9/6/294 |
| _version_ | 1797491072735117312 |
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| author | Zhuang Wang Weijun Pan Hui Li Xuan Wang Qinghai Zuo |
| author_facet | Zhuang Wang Weijun Pan Hui Li Xuan Wang Qinghai Zuo |
| author_sort | Zhuang Wang |
| collection | DOAJ |
| description | Deep reinforcement learning (DRL) has been widely adopted recently for its ability to solve decision-making problems that were previously out of reach due to a combination of nonlinear and high dimensionality. In the last few years, it has spread in the field of air traffic control (ATC), particularly in conflict resolution. In this work, we conduct a detailed review of existing DRL applications for conflict resolution problems. This survey offered a comprehensive review based on segments as (1) fundamentals of conflict resolution, (2) development of DRL, and (3) various applications of DRL in conflict resolution classified according to environment, model, algorithm, and evaluating indicator. Finally, an open discussion is provided that potentially raises a range of future research directions in conflict resolution using DRL. The objective of this review is to present a guidance point for future research in a more meaningful direction. |
| first_indexed | 2024-03-10T00:42:36Z |
| format | Article |
| id | doaj.art-62f43a646eb64a0aad193d73a3d82517 |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-10T00:42:36Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-62f43a646eb64a0aad193d73a3d825172023-11-23T15:05:18ZengMDPI AGAerospace2226-43102022-05-019629410.3390/aerospace9060294Review of Deep Reinforcement Learning Approaches for Conflict Resolution in Air Traffic ControlZhuang Wang0Weijun Pan1Hui Li2Xuan Wang3Qinghai Zuo4College of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, ChinaCollege of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, ChinaCollege of Computer Science, Sichuan University, Chengdu 610065, ChinaCollege of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, ChinaCollege of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, ChinaDeep reinforcement learning (DRL) has been widely adopted recently for its ability to solve decision-making problems that were previously out of reach due to a combination of nonlinear and high dimensionality. In the last few years, it has spread in the field of air traffic control (ATC), particularly in conflict resolution. In this work, we conduct a detailed review of existing DRL applications for conflict resolution problems. This survey offered a comprehensive review based on segments as (1) fundamentals of conflict resolution, (2) development of DRL, and (3) various applications of DRL in conflict resolution classified according to environment, model, algorithm, and evaluating indicator. Finally, an open discussion is provided that potentially raises a range of future research directions in conflict resolution using DRL. The objective of this review is to present a guidance point for future research in a more meaningful direction.https://www.mdpi.com/2226-4310/9/6/294air traffic controlconflict resolutiondeep reinforcement learning |
| spellingShingle | Zhuang Wang Weijun Pan Hui Li Xuan Wang Qinghai Zuo Review of Deep Reinforcement Learning Approaches for Conflict Resolution in Air Traffic Control Aerospace air traffic control conflict resolution deep reinforcement learning |
| title | Review of Deep Reinforcement Learning Approaches for Conflict Resolution in Air Traffic Control |
| title_full | Review of Deep Reinforcement Learning Approaches for Conflict Resolution in Air Traffic Control |
| title_fullStr | Review of Deep Reinforcement Learning Approaches for Conflict Resolution in Air Traffic Control |
| title_full_unstemmed | Review of Deep Reinforcement Learning Approaches for Conflict Resolution in Air Traffic Control |
| title_short | Review of Deep Reinforcement Learning Approaches for Conflict Resolution in Air Traffic Control |
| title_sort | review of deep reinforcement learning approaches for conflict resolution in air traffic control |
| topic | air traffic control conflict resolution deep reinforcement learning |
| url | https://www.mdpi.com/2226-4310/9/6/294 |
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