Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
7-2019
Abstract
We propose and implement a decentralized, intelligent air traffic flow management (ATFM) solution to improve the efficiency of air transportation in the ASEAN region as a whole. Our system, named BlockAgent, leverages the inherent synergy between multi-agent reinforcement learning (RL) for air traffic flow optimization; and the rising blockchain technology for a secure, transparent and decentralized coordination platform. As a result, BlockAgent does not require a centralized authority for effective ATFM operations. We have implemented several novel distributed coordination approaches for RL in BlockAgent. Empirical experiments with real air traffic data concerning regional airports have demonstrated the feasibility and effectiveness of our approach. To the best of our knowledge, this is the first work that considers blockchain-based, distributed RL for ATFM. Index Terms—decentralized optimization, air traffic flow management, blockchain, reinforcement learning, multi-agent systems
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
Proceedings of the 17th IEEE International Conference on Industrial Informatics, Helsinki-Espoo, Finland, 2019 July 22-25
Publisher
Elsevier: 12 months
City or Country
Helsinki-Espoo, Finland
Citation
TA, Nguyen Binh Duong; CHAUDHARY, Umang; and TRUONG, Hong-Linh.
Decentralizing air traffic flow management with blockchain based reinforcement learning. (2019). Proceedings of the 17th IEEE International Conference on Industrial Informatics, Helsinki-Espoo, Finland, 2019 July 22-25.
Available at: https://ink.library.smu.edu.sg/sis_research/4647
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.