Ivomar Brito Soares, Yann-Michaël De Hauwere, Kris Januarius, Tim Brys, Thierry Salvant, Ann Nowe
This paper considers how existing Reinforcement Learning (RL) techniques can be used to model and learn solutions for large scale Multi-Agent Systems (MAS). The large scale MAS of interest is the context of the movement of departure flights in big airports, commonly known as the Departure MAN-agement (DMAN) problem. A particular DMAN subproblem is how to respect Central Flow Management Unit (CFMU) take-off time windows, which are time windows planned by flow management authorities to be respected for the take-off time of departure flights. A RL model to handle this problem is proposed including the Markov Decision Process (MDP) definition, the behavior of the learning agents and how the problem can be modeled using RL ranging from the simplest to the full RL problem. Several experiments are also shown that illustrate the performance of the machine learning algorithm, with a comparison on how these problems are commonly handled by airport controllers nowadays. The environment in which the agents learn is provided by the Fast Time Simulator (FTS) AirTOp and the airport case study is the John F. Kennedy International Airport (KJFK) in New York City, USA, one of the busiest airports in the world.
Brito Soares, I, De Hauwere, Y-M, Januarius, K, Brys, T, Salvant, T & Nowe, A 2015, Departure MANagement with a Reinforcement Learning Approach: Respecting CFMU Slots. in 18th IEEE International Conference on Intelligent Transportation Systems (ITSC), 2015. IEEE, Las Palmas , pp. 1169-1176, IEEE 18th International Conference on Intelligent Transportation Systems, Las Palmas, Spain, 15/09/15. https://doi.org/10.1109/ITSC.2015.193
Brito Soares, I., De Hauwere, Y.-M., Januarius, K., Brys, T., Salvant, T., & Nowe, A. (2015). Departure MANagement with a Reinforcement Learning Approach: Respecting CFMU Slots. In 18th IEEE International Conference on Intelligent Transportation Systems (ITSC), 2015 (pp. 1169-1176). IEEE. https://doi.org/10.1109/ITSC.2015.193
@inproceedings{89c71bf4fbf94fc0bc030cfde85d3a78,
title = "Departure MANagement with a Reinforcement Learning Approach: Respecting CFMU Slots",
abstract = "This paper considers how existing Reinforcement Learning (RL) techniques can be used to model and learn solutions for large scale Multi-Agent Systems (MAS). The large scale MAS of interest is the context of the movement of departure flights in big airports, commonly known as the Departure MAN-agement (DMAN) problem. A particular DMAN subproblem is how to respect Central Flow Management Unit (CFMU) take-off time windows, which are time windows planned by flow management authorities to be respected for the take-off time of departure flights. A RL model to handle this problem is proposed including the Markov Decision Process (MDP) definition, the behavior of the learning agents and how the problem can be modeled using RL ranging from the simplest to the full RL problem. Several experiments are also shown that illustrate the performance of the machine learning algorithm, with a comparison on how these problems are commonly handled by airport controllers nowadays. The environment in which the agents learn is provided by the Fast Time Simulator (FTS) AirTOp and the airport case study is the John F. Kennedy International Airport (KJFK) in New York City, USA, one of the busiest airports in the world.",
keywords = "AirTOp, DMAN, Reinforcement Learning, Machine Learning, Airport, ATC",
author = "{Brito Soares}, Ivomar and {De Hauwere}, Yann-Micha{\"e}l and Kris Januarius and Tim Brys and Thierry Salvant and Ann Nowe",
year = "2015",
month = sep,
day = "15",
doi = "10.1109/ITSC.2015.193",
language = "English",
isbn = "978-1-4673-6595-6 ",
pages = "1169--1176",
booktitle = "18th IEEE International Conference on Intelligent Transportation Systems (ITSC), 2015",
publisher = "IEEE",
note = "IEEE 18th International Conference on Intelligent Transportation Systems ; Conference date: 15-09-2015 Through 18-09-2015",
}