An uncertainty-aware framework is presented for high-variance planning problems with multiple dynamic agents. Planning when surrounded by multiple uncertain dynamic agents is hard because one cannot be certain of either the initial states or the future actions of those agents, leading to an exponential explosion in possible futures. Many important real-world problems, such as autonomous driving, fit this model. To address these difficulties, Multi-policy Decision Making (MPDM) and Monte Carlo tree search (MCTS) are combined and a policy tree search performed with marginal action cost estimation and repeated belief particles.


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    Titel :

    Monte Carlo Policy Tree Decision Making


    Beteiligte:
    OLSON EDWIN (Autor:in) / HAGGENMILLER ACSHI (Autor:in)

    Erscheinungsdatum :

    2023-06-08


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Englisch


    Klassifikation :

    IPC:    B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen



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