Deep Reinforcement Learning Algorithms for Steering a Underactuated Ship

Le Pham Tuyen, Layek Abu, Ngo Anh Vien, TaeChoong Chung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)
827 Downloads (Pure)

Abstract

Based on state-of-the-art deep reinforcement learning (Deep RL) algorithms, two controllers are proposed to pass a ship through a specified gate. Deep RL is a powerful approach to learn a complex controller which is expected to adapt to different situations of systems. This paper explains how to apply these algorithms to ship steering problem. The simulation results show advantages of these algorithms in reproducing reliable and stable controllers
Original languageEnglish
Title of host publication2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017)
Number of pages6
ISBN (Electronic)978-1-5090-6064-1, 978-1-5090-6063-4
DOIs
Publication statusPublished - 11 Dec 2017
Event2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017) - , Korea, Republic of
Duration: 16 Nov 201718 Nov 2017

Conference

Conference2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017)
CountryKorea, Republic of
Period16/11/201718/11/2017

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  • Cite this

    Pham Tuyen, L., Abu, L., Vien, N. A., & Chung, T. (2017). Deep Reinforcement Learning Algorithms for Steering a Underactuated Ship. In 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017) https://doi.org/10.1109/MFI.2017.8170388