This paper aims to examine the potential of using the emerging deep reinforcement learning techniques in missile guidance applications. To this end, a Markovian decision process that enables the application of reinforcement learning theory to solve the guidance problem is formulated. A heuristic way is used to shape a proper reward function that has tradeoff between guidance accuracy, energy consumption, and interception time. The state-of-the-art deep deterministic policy gradient algorithm is used to learn an action policy that maps the observed engagements states to a guidance command. Extensive empirical numerical simulations are performed to validate the proposed computational guidance algorithm.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Computational Missile Guidance: A Deep Reinforcement Learning Approach


    Beteiligte:
    He, Shaoming (Autor:in) / Shin, Hyo-Sang (Autor:in) / Tsourdos, Antonios (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.08.2021




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Multi-missile Cooperative Guidance Law Based on Deep Reinforcement Learning

    Li, Chengxuan / Cheng, Haoyu / Wang, Junrui et al. | Springer Verlag | 2024



    Missile Homing-Phase Guidance Law Design Using Reinforcement Learning

    Gaudet, B. / Furfaro, R. / American Institute of Aeronautics and Astronautics | British Library Conference Proceedings | 2012