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, (CAMS-2024), 03.9.-05.9.2024, Blacksburg, Virginia, n.n., Sep/2024.

Abstract :

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.

Keywords :

Unmanned marine vehicles, Reinforcement learning control, Trajectory Tracking and Path Following, Sim-to-Real Transfer, Domain Randomization


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last updated 28.02.2023
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