Searching indoor environments in the presence of unknown obstacles with multiple UAV agents remains a challenge. This paper presents a framework for target-finding using a combination of traditional POMDP based planning and Deep Reinforcement Learning. The implementation breaks the problem into two separate stages of planning and control, with both stages modelled as a Partially Observable Markov Decision Process (POMDP). Global decentralised planning is provided using a modern online POMDP solver, while a modern Deep Reinforcement Learning algorithm is used to provide a policy for local control. Our results indicate that such a framework is capable of target-finding within a simulated indoor test environment in the presence of unknown obstacles, and once extended to real-world operation could enable UAVs to be applied in an increasing number of applications.


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    Titel :

    Multi-UAV Target-Finding in Simulated Indoor Environments using Deep Reinforcement Learning


    Beteiligte:
    Walker, Ory (Autor:in) / Vanegas, Fernando (Autor:in) / Gonzalez, Felipe (Autor:in) / Koenig, Sven (Autor:in)


    Erscheinungsdatum :

    01.03.2020


    Format / Umfang :

    8224728 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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