Data analytics for air travel data: a survey and new perspectives
From the start, the airline industry has remarkably connected countries all over the world
through rapid long-distance transportation, helping people overcome geographic barriers …
through rapid long-distance transportation, helping people overcome geographic barriers …
A survey on reinforcement learning in aviation applications
Reinforcement learning (RL) has emerged as a powerful tool for addressing complex
decision making problems in various domains, including aviation. This paper provides a …
decision making problems in various domains, including aviation. This paper provides a …
Explaining deep reinforcement learning decisions in complex multiagent settings: towards enabling automation in air traffic flow management
T Kravaris, K Lentzos, G Santipantakis, GA Vouros… - Applied …, 2023 - Springer
With the objective to enhance human performance and maximize engagement during the
performance of tasks, we aim to advance automation for decision making in complex and …
performance of tasks, we aim to advance automation for decision making in complex and …
Hierarchical multiagent reinforcement learning schemes for air traffic management
C Spatharis, A Bastas, T Kravaris, K Blekas… - Neural Computing and …, 2023 - Springer
In this work we investigate the use of hierarchical multiagent reinforcement learning
methods for the computation of policies to resolve congestion problems in the air traffic …
methods for the computation of policies to resolve congestion problems in the air traffic …
[HTML][HTML] Locally generalised multi-agent reinforcement learning for demand and capacity balancing with customised neural networks
Reinforcement Learning (RL) techniques are being studied to solve the Demand and
Capacity Balancing (DCB) problems to fully exploit their computational performance. A …
Capacity Balancing (DCB) problems to fully exploit their computational performance. A …
[HTML][HTML] General multi-agent reinforcement learning integrating heuristic-based delay priority strategy for demand and capacity balancing
Reinforcement learning (RL) techniques have been studied for solving the demand and
capacity balancing (DCB) problem in air traffic management to exploit their full …
capacity balancing (DCB) problem in air traffic management to exploit their full …
Multi-agent deep reinforcement learning for solving large-scale air traffic flow management problem: A time-step sequential decision approach
In this paper, we focus on the demand-capacity balancing (DCB) problem in air traffic flow
management, which is considered as a fully cooperative multi-agent learning task. First, a …
management, which is considered as a fully cooperative multi-agent learning task. First, a …
Image-based multi-agent reinforcement learning for demand–capacity balancing
S Mas-Pujol, E Salamí, E Pastor - Aerospace, 2022 - mdpi.com
Air traffic flow management (ATFM) is of crucial importance to the European Air Traffic
Control System due to two factors: first, the impact of ATFM, including safety implications on …
Control System due to two factors: first, the impact of ATFM, including safety implications on …
Multiagent reinforcement learning methods for resolving demand-capacity imbalances
T Kravaris, C Spatharis, K Blekas… - 2018 IEEE/AIAA 37th …, 2018 - ieeexplore.ieee.org
In this article, we explore the computation of joint policies for autonomous agents,
representing flights, to resolve congestions problems in the Air Traffic Management (ATM) …
representing flights, to resolve congestions problems in the Air Traffic Management (ATM) …
Xdqn: Inherently interpretable dqn through mimicking
A Kontogiannis, G Vouros - arXiv preprint arXiv:2301.03043, 2023 - arxiv.org
Although deep reinforcement learning (DRL) methods have been successfully applied in
challenging tasks, their application in real-world operational settings is challenged by …
challenging tasks, their application in real-world operational settings is challenged by …