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) domain in the context of Demand-Capacity Balance (DCB) process. We formalize the problem as a multi-agent Markov Decision Process (MDP) towards deciding flight ground delays to resolve imbalances, during the pre-tactical phase. To this end, we present and evaluate multi-agent reinforcement learning methods. An experimental study on real-world cases confirms the effectiveness of our approach.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Multiagent Reinforcement Learning Methods for Resolving Demand - Capacity Imbalances




    Erscheinungsdatum :

    01.09.2018


    Format / Umfang :

    1528804 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Multiagent Reinforcement Learning-Based Taxi Predispatching Model to Balance Taxi Supply and Demand

    Yongjian Yang / Xintao Wang / Yuanbo Xu et al. | DOAJ | 2020

    Freier Zugriff

    Network Model to Address Capacity/Demand Imbalances in the National Airspace System

    Myers, T. / Kierstead, D. / American Institute of Aeronautics and Astronautics | British Library Conference Proceedings | 2008



    A Social View to Multiagent Reinforcement Learning

    Wei, Q. / Sawaragi, T. | British Library Online Contents | 2004