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Controlling a cargo ship without human experience based on deep Q-network

Chen, C; Feng, M; Jialun, L; Negenborn, RR; Liu, Y; Yan, X; (2020) Controlling a cargo ship without human experience based on deep Q-network. Journal of Intelligent and Fuzzy Systems 10.3233/JIFS-200754. (In press). Green open access

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Abstract

Human experience is regarded as an indispensable part of artificial intelligence in the process of controlling or decision making for autonomous cargo ships. In this paper, a novel Deep Q-Network-based (DQN) approach is proposed, which performs satisfactorily in controlling a cargo ship automatically without any human experience. At the very beginning, we use the model of KRISO Very Large Crude Carrier (KVLCC2) to describe a cargo ship. To manipulate this ship has to conquer great inertia and relatively insufficient driving force. Subsequently, customary waterways, regulations, conventions are described with Artificial Potential Field and value-functions in DQN. Based on this, the artificial intelligence of planning and controlling a cargo ship can be obtained by undertaking sufficient training, which can control the ship directly, while avoiding collisions, keeping its position in the middle of the route as much as possible. In simulation experiments, it is demonstrated that such an approach performs better than manual works and other traditional methods in most conditions, which makes the proposed method a promising solution in improving the autonomy level of cargo ships.

Type: Article
Title: Controlling a cargo ship without human experience based on deep Q-network
Open access status: An open access version is available from UCL Discovery
DOI: 10.3233/JIFS-200754
Publisher version: https://doi.org/10.3233/JIFS-200754
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Deep Q-network, reinforcement learning, artificial intelligence, autonomous ships
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering
URI: https://discovery.ucl.ac.uk/id/eprint/10106869
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