Variational Autoencoders for exteroceptive perception in reinforcement
  learning-based collision avoidance
Thomas Nakken Larsen Eirik Runde Barlaug Adil Rasheed
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.