  Autonomous systems are becoming ubiquitous and gaining momentum within the
marine sector. Since the electrification of transport is happening
simultaneously, autonomous marine vessels can reduce environmental impact,
lower costs, and increase efficiency. Although close monitoring is still
required to ensure safety, the ultimate goal is full autonomy. One major
milestone is to develop a control system that is versatile enough to handle any
weather and encounter that is also robust and reliable. Additionally, the
control system must adhere to the International Regulations for Preventing
Collisions at Sea (COLREGs) for successful interaction with human sailors.
Since the COLREGs were written for the human mind to interpret, they are
written in ambiguous prose and therefore not machine-readable or verifiable.
Due to these challenges and the wide variety of situations to be tackled,
classical model-based approaches prove complicated to implement and
computationally heavy. Within machine learning (ML), deep reinforcement
learning (DRL) has shown great potential for a wide range of applications. The
model-free and self-learning properties of DRL make it a promising candidate
for autonomous vessels. In this work, a subset of the COLREGs is incorporated
into a DRL-based path following and obstacle avoidance system using collision
risk theory. The resulting autonomous agent dynamically interpolates between
path following and COLREG-compliant collision avoidance in the training
scenario, isolated encounter situations, and AIS-based simulations of
real-world scenarios.
