Realistic Curriculum Reinforcement Learning for Autonomous and Sustainable Marine Vessel Navigation

• URL: http://arxiv.org/abs/2601.10911v1
• Date: Thu, 15 Jan 2026 23:51:57 GMT
• Title: Realistic Curriculum Reinforcement Learning for Autonomous and Sustainable Marine Vessel Navigation
• Authors: Zhang Xiaocai, Xiao Zhe, Liang Maohan, Liu Tao, Li Haijiang, Zhang Wenbin,
• Abstract summary: We propose a Curriculum Reinforcement Learning framework integrated with a realistic, data-driven marine simulation environment.<n>Vessel fuel consumption is estimated using historical operational data and learning-based regression.<n>We design a lightweight, policy-based CRL agent with a comprehensive reward mechanism that considers safety, emissions, timeliness, and goal completion.
• Score: 0.5439021491474986
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sustainability is becoming increasingly critical in the maritime transport, encompassing both environmental and social impacts, such as Greenhouse Gas (GHG) emissions and navigational safety. Traditional vessel navigation heavily relies on human experience, often lacking autonomy and emission awareness, and is prone to human errors that may compromise safety. In this paper, we propose a Curriculum Reinforcement Learning (CRL) framework integrated with a realistic, data-driven marine simulation environment and a machine learning-based fuel consumption prediction module. The simulation environment is constructed using real-world vessel movement data and enhanced with a Diffusion Model to simulate dynamic maritime conditions. Vessel fuel consumption is estimated using historical operational data and learning-based regression. The surrounding environment is represented as image-based inputs to capture spatial complexity. We design a lightweight, policy-based CRL agent with a comprehensive reward mechanism that considers safety, emissions, timeliness, and goal completion. This framework effectively handles complex tasks progressively while ensuring stable and efficient learning in continuous action spaces. We validate the proposed approach in a sea area of the Indian Ocean, demonstrating its efficacy in enabling sustainable and safe vessel navigation.

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