  Modern control systems are increasingly turning to machine learning
algorithms to augment their performance and adaptability. Within this context,
Deep Reinforcement Learning (DRL) has emerged as a promising control framework,
particularly in the domain of marine transportation. Its potential for
autonomous marine applications lies in its ability to seamlessly combine
path-following and collision avoidance with an arbitrary number of obstacles.
However, current DRL algorithms require disproportionally large computational
resources to find near-optimal policies compared to the posed control problem
when the searchable parameter space becomes large. To combat this, our work
delves into the application of Variational AutoEncoders (VAEs) to acquire a
generalized, low-dimensional latent encoding of a high-fidelity range-finding
sensor, which serves as the exteroceptive input to a DRL agent. The agent's
performance, encompassing path-following and collision avoidance, is
systematically tested and evaluated within a stochastic simulation environment,
presenting a comprehensive exploration of our proposed approach in maritime
control systems.
