Despite the increasing adoption of Deep Reinforcement Learning (DRL) for Autonomous Surface Vehicles (ASVs), there still remain challenges limiting real-world deployment. In this paper, we first integrate buoyancy and hydrodynamics models into a modern Reinforcement Learning framework to reduce training time. Next, we show how system identification coupled with domain randomization improves the RL agent performance and narrows the sim-to-real gap. Real-world experiments for the task of capturing floating waste show that our approach lowers energy consumption by 13.1\% while reducing task completion time by 7.4\%. These findings, supported by sharing our open-source implementation, hold the potential to impact the efficiency and versatility of ASVs, contributing to environmental conservation efforts.

翻译：尽管深度强化学习（DRL）在自主水面艇（ASV）中的应用日益增多，但其实际部署仍面临诸多挑战。本文首先将浮力与流体动力学模型集成到现代强化学习框架中，以减少训练时间。其次，我们展示了系统辨识与领域随机化相结合如何提升强化学习智能体的性能并缩小仿真与现实之间的差距。针对捕获漂浮废弃物任务的真实世界实验表明，我们的方法在降低任务完成时间7.4%的同时，减少了13.1%的能耗。这些发现，辅以我们开源实现的共享，有望提升ASV的效率和多功能性，从而为环境保护事业做出贡献。