IMLRLS: a method for ship collision avoidance by integrating meta-learning with reinforcement learning

Xinyu Jia; Shu Gao; Wei He

doi:10.1117/12.3032980

5 July 2024 IMLRLS: a method for ship collision avoidance by integrating meta-learning with reinforcement learning

Xinyu Jia, Shu Gao, Wei He

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131842Q (2024) https://doi.org/10.1117/12.3032980
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

Autonomous collision avoidance is vital for intelligent ship navigation. To improve the adaptability and effectiveness of collision avoidance policies, we propose a method that integrates meta-learning with reinforcement learning. Drawing inspiration from meta-learning, we developed a two-layered recurrent model to boost the adaptability and effectiveness of these policies. We then introduced a ship motion model considering wind, waves, and currents to better adapt to environmental changes. Subsequently, we designed a noise-enhanced autonomous ship collision avoidance reinforcement learning model to augment the exploration capabilities of the vessel agents. Lastly, simulation experiments were conducted to validate the feasibility of our approach. The results demonstrate that our collision avoidance policies surpass various comparative methods, showing superior effectiveness and adaptability. Overall, our innovative method offers a safer solution for enhancing intelligent ship navigation.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xinyu Jia, Shu Gao, and Wei He "IMLRLS: a method for ship collision avoidance by integrating meta-learning with reinforcement learning", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131842Q (5 July 2024); https://doi.org/10.1117/12.3032980

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