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.
