A Deep Reinforcement Learning Framework and Methodology for Reducing the Sim-to-Real Gap in ASV Navigation
<|reference_start|>A Deep Reinforcement Learning Framework and Methodology for Reducing the Sim-to-Real Gap in ASV Navigation: 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.<|reference_end|>
@article{batista2024a,
    title={A Deep Reinforcement Learning Framework and Methodology for Reducing the
  Sim-to-Real Gap in ASV Navigation},
    author={Luis F W Batista (UL), Junghwan Ro, Antoine Richard, Pete Schroepfer,
  Seth Hutchinson, Cedric Pradalier},
    journal={arXiv preprint arXiv:2407.08263},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.08263},
    primaryClass={cs.RO}
}