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Title page for ETD etd-07272005-114235


Type of Document Master's Thesis
Author Conn, Karla Gail
Author's Email Address karla.g.conn@vanderbilt.edu
URN etd-07272005-114235
Title Supervised-reinforcement learning for a mobile robot in a real-world environment
Degree Master of Science
Department Electrical Engineering
Advisory Committee
Advisor Name Title
Richard Alan Peters, II Committee Chair
Douglas Fisher Committee Member
Keywords
  • reinforcement learning
  • mobile robot
  • intelligent machine
Date of Defense 2005-07-18
Availability unrestricted
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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