Review of deep reinforcement learning approaches for conflict resolution in air traffic control
Z Wang, W Pan, H Li, X Wang, Q Zuo - Aerospace, 2022 - mdpi.com
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 …
decision-making problems that were previously out of reach due to a combination of …
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 …
Deep reinforcement learning based path stretch vector resolution in dense traffic with uncertainties
With the continuous growth in the air transportation demand, air traffic controllers will have to
handle increased traffic and consequently, more potential conflicts. This gives rise to the …
handle increased traffic and consequently, more potential conflicts. This gives rise to the …
Representation uncertainty in self-supervised learning as variational inference
H Nakamura, M Okada… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
In this study, a novel self-supervised learning (SSL) method is proposed, which considers
SSL in terms of variational inference to learn not only representation but also representation …
SSL in terms of variational inference to learn not only representation but also representation …
Towards conformal automation in air traffic control: Learning conflict resolution strategies through behavior cloning
A critical factor in achieving conformity of automation tools in performing expert tasks, such
as air traffic conflict resolution, is the identification of air traffic controllers'(ATCOs') …
as air traffic conflict resolution, is the identification of air traffic controllers'(ATCOs') …
Integrated conflict management for uam with strategic demand capacity balancing and learning-based tactical deconfliction
Urban air mobility (UAM) has the potential to revolutionize our daily transportation, offering
rapid and efficient deliveries of passengers and cargo between dedicated locations within …
rapid and efficient deliveries of passengers and cargo between dedicated locations within …
Safety validation for deep reinforcement learning based aircraft separation assurance with adaptive stress testing
Ensuring safe separation of aircraft has become a major challenge given the growing
demand for air transportation. Recently, deep reinforcement learning (DRL) has been …
demand for air transportation. Recently, deep reinforcement learning (DRL) has been …
General multi-agent reinforcement learning integrating adaptive manoeuvre strategy for real-time multi-aircraft conflict resolution
Reinforcement learning (RL) techniques are under investigation for resolving conflict in air
traffic management (ATM), exploiting their computational capabilities and ability to cope with …
traffic management (ATM), exploiting their computational capabilities and ability to cope with …
[HTML][HTML] General real-time three-dimensional multi-aircraft conflict resolution method using multi-agent reinforcement learning
Reinforcement learning (RL) techniques have been studied for solving the conflict resolution
(CR) problem in air traffic management, leveraging their potential for computation and ability …
(CR) problem in air traffic management, leveraging their potential for computation and ability …
Estimating airspace resource capacity for advanced air mobility operations
View Video Presentation: https://doi. org/10.2514/6.2022-3317. vid Demand capacity
balancing (DCB) has been proposed as a strategic mechanism to balance efficiency and …
balancing (DCB) has been proposed as a strategic mechanism to balance efficiency and …