The Application of Reinforcement Learning (RL) in Autonomous Ship and Collision Avoidance: A Systematic Literature Review

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Insinööritieteiden korkeakoulu | Bachelor's thesis
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Date

2024-04-26

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Major/Subject

Computational Engineering

Mcode

ENG3082

Degree programme

Aalto Bachelor’s Programme in Science and Technology

Language

en

Pages

23 +3

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Abstract

In recent years, research on the application of reinforcement learning (RL) to improve the intelligence of ships has increased significantly. This study presents a systematic literature review that examines the applications of RL in the field of autonomous ships and collision avoidance. In particular, it answers the research questions of what different RL models are used, what different inputs they process and what range of outputs they produce. In addition, the simulation environments and the choice of hyperparameters that researchers use to train these models are studied. Finally, this study highlights the current implementation challenges in this area to ensure the safe and robust use of RL models in autonomous vessels.

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Supervisor

St-Pierr , Luc

Thesis advisor

Musharraf, Mushrura

Keywords

reinforcement learning, deep reinforcement learning, deep Q-network, autonomous ship, collision avoidance, own ship

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https://urn.fi/URN:NBN:fi:aalto-202406184533

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[kand] Insinööritieteiden korkeakoulu / ENG