  Collision avoidance algorithms for Autonomous Surface Vehicles (ASV) that
follow the Convention on the International Regulations for Preventing
Collisions at Sea (COLREGs) have been proposed in recent years. However, it may
be difficult and unsafe to follow COLREGs in congested waters, where multiple
ASVs are navigating in the presence of static obstacles and strong currents,
due to the complex interactions. To address this problem, we propose a
decentralized multi-ASV collision avoidance policy based on Distributional
Reinforcement Learning, which considers the interactions among ASVs as well as
with static obstacles and current flows. We evaluate the performance of the
proposed Distributional RL based policy against a traditional RL-based policy
and two classical methods, Artificial Potential Fields (APF) and Reciprocal
Velocity Obstacles (RVO), in simulation experiments, which show that the
proposed policy achieves superior performance in navigation safety, while
requiring minimal travel time and energy. A variant of our framework that
automatically adapts its risk sensitivity is also demonstrated to improve ASV
safety in highly congested environments.
