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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    Title :

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


    Contributors:
    Kuhnert, K.-D. (author) / Krodel, M. (author)


    Publication date :

    2005-01-01


    Size :

    224144 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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

    Kroedel, M. / Kuhnert, K.-D. / IEEE | British Library Conference Proceedings | 2005


    Autonomous vehicle steering based on evaluative feedback by reinforcement learning

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



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