Steering an autonomous vehicle requires the permanent adaptation of behavior in relationship to the various situations the vehicle is in. This paper describes a research which implements such adaptation and optimization based on reinforcement learning (RL) which in detail purely learns from evaluative feedback in contrast to instructive feedback. In this way it self-explores and self-optimises actions for situations in a defined environment. The target of this research is to determine to what extent RL-based systems serve as an enhancement or even an alternative to classical concepts of autonomous intelligent vehicles such as modelling or neural nets.


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    Titel :

    Evaluative feedback as the basis for behavior optimization in the of autonomous vehicle steering


    Beteiligte:
    Kuhnert, K.-D. (Autor:in) / Krodel, M. (Autor:in)


    Erscheinungsdatum :

    01.01.2005


    Format / Umfang :

    224144 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Evaluative Feedback As the Basis for Behavior Optimization in the Area of Autonomous Vehicle Steering

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    Kuhnert, Klaus-Dieter / Krodel, Michael | Tema Archiv | 2005



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