In this work, we proposed and developed a reinforcement Q-learning method to do the lane-keeping and obstacle evasion driving maneuvers. We detail how to design a simple car simulator and how to use it to do the training. For each problem, we define different states, actions, and reward functions to obtain a Q-table. Next, we use it as a driving maneuver controller in a different simulation environment. With this method, our car successfully droves on a road different to where it was training. An important conclusion is the possibility to build, more complex controllers to do passing or behavior selectors.


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    Titel :

    Q-Learning for Autonomous Vehicle Navigation




    Erscheinungsdatum :

    15.11.2023


    Format / Umfang :

    1018528 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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