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Deep Reinforcement Learning for Path-Following Control of an Autonomous Surface Vehicle using Domain Randomization

Tom Vincent Slawik; Bilal Wehbe; Leif Christensen
In: 15th IFAC Conference on Control Applications in Marine Systems, Robotics and Vehicles, 2024. IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles (CAMS-2024), September 3-5, Blacksburg, Virginia, USA, n.n. 9/2024.


In this paper, we propose a path-following controller for an autonomous surface vehicle (ASV) that is based on model-free deep reinforcement learning. To make the learning agent more robust, we investigate domain randomization for sim-to-real transfer. We provide a comparison between three different algorithms: Deep Deterministic Policy Gradient (DDPG), Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO). The trained models are evaluated on the small-scale ASV Altus-LSA Niriis in the maritime test basin at DFKI RIC, Germany. Our results show that applying domain randomization leads to a significant performance improvement compared to no domain randomization, when tested on real hardware.