Recent applications of deep reinforcement learning in controlling maritime autonomoussurface vessels have shown promise for integration into maritime transportation. These could have the potential to reduce at-sea incidents such as collisions and groundings which are majorly attributed to human error. With this in mind the goal of this work is to evaluate how well a similar deep reinforcement learning agent could perform the same task in submarines but using passive SONAR rather than the ranging data provided by active RADAR aboard surface vessels. A simulated submarine outfitted with a passive spherical, hull-mounted SONAR sensor is placed into contact scenarios under the control of a reinforcement learning agent and directed to make its way to a navigational waypoint while avoiding interfering surface vessels. In order to see how this best translates to lower power autonomous vessels (vice warship submarines), no estimation for the range of the surface vessels is maintained in order to cut down on computing requirements. Inspired by my time aboard U.S. Navy submarines, the agent is provided with simply the simulated passive SONAR data. I show that this agent is capable of navigating to a waypoint while avoiding crossing, overtaking, and head-on surface vessels and thus could provide a recommended course to a submarine contact management team in ample time since the maneuvers made by the agent are not instantaneous in contrast to the assumptions of traditional target tracking with bearing-only data. Additionally, an in-progress plugin for Epic Games’ Unreal Engine is presented with the ability to simulate underwater acoustics inside the 3D development software. Unreal Engine is a powerful 3D game engine that is incredibly flexible and capable of being integrated into many different forms of scientific research. This plugin could provide researchers with the ability to conduct useful simulations in intuitively designed 3D environments.