Controlling a cargo ship without human experience using deep Q-network

Chen Chen, Feng Ma*, Jialun Liu, Rudy R. Negenborn, Yuanchang Liu, Xinping Yan

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

5 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)7363-7379
JournalJournal of Intelligent and Fuzzy Systems
Issue number5
Publication statusPublished - 2020


  • artificial intelligence
  • autonomous ships
  • Deep Q-network
  • reinforcement learning


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