  Human error is a substantial factor in marine accidents, accounting for 85%
of all reported incidents. By reducing the need for human intervention in
vessel navigation, AI-based methods can potentially reduce the risk of
accidents. AI techniques, such as Deep Reinforcement Learning (DRL), have the
potential to improve vessel navigation in challenging conditions, such as in
restricted waterways and in the presence of obstacles. This is because DRL
algorithms can optimize multiple objectives, such as path following and
collision avoidance, while being more efficient to implement compared to
traditional methods. In this study, a DRL agent is trained using the Deep
Deterministic Policy Gradient (DDPG) algorithm for path following and waypoint
tracking. Furthermore, the trained agent is evaluated against a traditional PD
controller with an Integral Line of Sight (ILOS) guidance system for the same.
This study uses the Kriso Container Ship (KCS) as a test case for evaluating
the performance of different controllers. The ship's dynamics are modeled using
the maneuvering Modelling Group (MMG) model. This mathematical simulation is
used to train a DRL-based controller and to tune the gains of a traditional PD
controller. The simulation environment is also used to assess the controller's
effectiveness in the presence of wind.
