Electronic Theses for Schreyer Honors College
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Author Last Name
Area of Honors
Maritime Navigation and Contact Avoidance through Reinforcement Learning
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Davis, Steven Lee
Area of Honors:
Bachelor of Science
John Phillip Sustersic, Jr., Thesis Supervisor
Vijaykrishnan Narayanan, Honors Advisor
unmanned underwater vehicle
autonomous underwater vehicle
This thesis explores the potential for applying reinforcement learning to provide autonomous navigation and contact avoidance to an unmanned underwater vehicle. A major area of interest is using reinforcement learning for the navigation of land vehicles, but few works explore these techniques in a maritime setting, where control and sensing of the vehicle function much differently. Additionally, previous works in the maritime setting have mainly focused on control systems or relied on potentially unrealistic sensor information. Operating on purely relational measurements, this thesis explores deep Q-Learning, experience replay, and reward shaping in pursuit of achieving autonomous navigation and contact avoidance. It demonstrates the potential of these reinforcement learning algorithms by successfully inducing a simulated underwater vehicle to navigate to its objective without detection by enemy contacts.
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