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.

翻译：近年来，已有研究提出遵循《国际海上避碰规则公约》（COLREGs）的自主水面船舶（ASV）避碰算法。然而，在存在静态障碍物和强水流的拥挤水域中，多艘ASV同时航行时，由于复杂交互作用，遵循COLREGs可能既困难又不安全。针对该问题，本文提出一种基于分布式强化学习的去中心化多ASV避碰策略，该策略同时考虑了ASV之间、ASV与静态障碍物及水流之间的相互作用。通过仿真实验，我们将所提基于分布式强化学习的策略与传统强化学习策略、人工势场法（APF）和互惠速度障碍法（RVO）两种经典方法进行了性能评估。结果表明，所提策略在导航安全性上表现优越，同时所需航行时间和能耗最小。此外，我们提出的自适应风险敏感性框架变体，在高度拥挤环境中进一步提升了ASV的安全性。