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    Supervised-reinforcement learning for a mobile robot in a real-world environment

    Conn, Karla Gail
    : https://etd.library.vanderbilt.edu/etd-07272005-114235
    http://hdl.handle.net/1803/13631
    : 2005-07-28

    Abstract

    This research measures how well supervised-reinforcement-learning techniques perform when applied to real-world tasks, managed as a discrete-event dynamic system (DEDS). Two types of experiments are tested. One tests the robot’s stability in implementing a task it has been taught. The other experiment includes obstacles blocking the path to the goal and measures the robot’s flexibility. The supervisor consists of human-guided remote-controlled runs through the navigation task and acts as a teacher for the initial stages of reinforcement learning. Experimental analysis is based on measurements of average time to reach the goal and the number of failed states encountered during a trial of episodes.
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