This paper develops a Deep Reinforcement Learning (DRL)-agent for navigation and control of autonomous surface vessels (ASV) on inland waterways. Spatial restrictions due to waterway geometry and the resulting challenges, such as high flow velocities or shallow banks, require controlled and precise movement of the ASV. A state-of-the-art bootstrapped Q-learning algorithm in combination with a versatile training environment generator leads to a robust and accurate rudder controller. To validate our results, we compare the path-following capabilities of the proposed approach to a vessel-specific PID controller on real-world river data from the lower- and middle Rhine, indicating that the DRL algorithm could effectively prove generalizability even in never-seen scenarios while simultaneously attaining high navigational accuracy.

翻译：本文开发了一种深度强化学习智能体，用于内陆水域自主水面船舶的导航与控制。由于河道几何形状造成的空间限制及由此产生的挑战，如高流速或浅滩，要求对自主水面船舶进行受控且精确的运动。采用最先进的自助Q学习算法结合通用训练环境生成器，实现了鲁棒且精确的舵机控制器。为验证结果，我们将所提方法的路径跟踪能力与基于真实莱茵河中下游河流数据的船舶专用PID控制器进行了对比，表明深度强化学习算法即使在未见场景中也能有效证明其泛化能力，同时保持高导航精度。