Το work with title Learning policies for resolving demand-capacity imbalances during pre-tactical air traffic management by Kravaris Theocharis, Vouros, George A, Spatharis Christos, Blekas, Konstantinos D, Chalkiadakis Georgios, Garcia Jose Manuel Cordero is licensed under Creative Commons Attribution 4.0 International
Bibliographic Citation
T. Kravaris, G. A. Vouros, C. Spatharis, K. Blekas, G. Chalkiadakis and J. M. C. Garcia, "Learning policies for resolving demand-capacity imbalances during pre-tactical air traffic management, " in 15th German Conference on Multiagent System Technologies, 2017, pp. 238-255. doi: 10.1007/978-3-319-64798-2_15
https://doi.org/10.1007/978-3-319-64798-2_15
In this work we propose and investigate the use of collaborative reinforcement learning methods for resolving demand-capacity imbalances during pre-tactical Air Traffic Management. By so doing, we also initiate the study of data-driven techniques for predicting multiple correlated aircraft trajectories; and, as such, respond to a need identified in contemporary research and practice in air-traffic management. Our simulations, designed based on real-world data, confirm the effectiveness of our methods in resolving the demand-capacity problem, even in extremely hard scenarios.