Rolling Cargo Management Using a Deep Reinforcement Learning Approach

Rachid Oucheikh, Tuwe Löfström, Ernst Ahlberg and Lars Carlsson
Additional contact information
Rachid Oucheikh: Department of Computer Science, Jönköping University, 553 18 Jönköping, Sweden
Tuwe Löfström: Department of Computer Science, Jönköping University, 553 18 Jönköping, Sweden
Ernst Ahlberg: Department of Pharmaceutical Biosciences, Uppsala University, 752 36 Uppsala, Sweden
Lars Carlsson: Stena Line, 413 27 Göteborg, Sweden

Logistics, 2021, vol. 5, issue 1, 1-18

Abstract: Loading and unloading rolling cargo in roll-on/roll-off are important and very recurrent operations in maritime logistics. In this paper, we apply state-of-the-art deep reinforcement learning algorithms to automate these operations in a complex and real environment. The objective is to teach an autonomous tug master to manage rolling cargo and perform loading and unloading operations while avoiding collisions with static and dynamic obstacles along the way. The artificial intelligence agent, representing the tug master, is trained and evaluated in a challenging environment based on the Unity3D learning framework, called the ML-Agents, and using proximal policy optimization. The agent is equipped with sensors for obstacle detection and is provided with real-time feedback from the environment thanks to its own reward function, allowing it to dynamically adapt its policies and navigation strategy. The performance evaluation shows that by choosing appropriate hyperparameters, the agents can successfully learn all required operations including lane-following, obstacle avoidance, and rolling cargo placement. This study also demonstrates the potential of intelligent autonomous systems to improve the performance and service quality of maritime transport.

Keywords: deep reinforcement learning; cargo management for roll-on/roll-off ships; autonomous tug master; agent based reinforcement learning; collision avoidance (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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