  Human decision-making errors cause a majority of globally reported marine
accidents. As a result, automation in the marine industry has been gaining more
attention in recent years. Obstacle avoidance becomes very challenging for an
autonomous surface vehicle in an unknown environment. We explore the
feasibility of using Deep Q-Learning (DQN), a deep reinforcement learning
approach, for controlling an underactuated autonomous surface vehicle to follow
a known path while avoiding collisions with static and dynamic obstacles. The
ship's motion is described using a three-degree-of-freedom (3-DOF) dynamic
model. The KRISO container ship (KCS) is chosen for this study because it is a
benchmark hull used in several studies, and its hydrodynamic coefficients are
readily available for numerical modelling. This study shows that Deep
Reinforcement Learning (DRL) can achieve path following and collision avoidance
successfully and can be a potential candidate that may be investigated further
to achieve human-level or even better decision-making for autonomous marine
vehicles.
