Realistic Curriculum Reinforcement Learning for Autonomous and Sustainable Marine Vessel Navigation

2026-01-15

AI Summary

This study addresses the safety and environmental risks inherent in traditional ship navigation, which heavily relies on human expertise and is prone to operational errors. To overcome these limitations, the authors propose an autonomous navigation framework that integrates curriculum reinforcement learning, diffusion model–driven high-fidelity maritime simulation, and data-driven fuel consumption prediction. The approach uniquely combines curriculum reinforcement learning with dynamic sea condition modeling—derived from real-world trajectory data—and a multi-objective reward function that jointly optimizes safety, schedule adherence, and emissions reduction. Leveraging image-based environmental perception and continuous-action policy optimization, the system enables adaptive decision-making under realistic maritime conditions. Experimental validation in the Indian Ocean demonstrates significant improvements in navigational safety and energy efficiency, achieving robust and sustainable autonomous vessel operation.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Learning to Search
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

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.

Problem

• Autonomous Navigation
• Sustainable Maritime Transport
• Greenhouse Gas Emissions
• Navigational Safety
• Marine Vessel

Innovation

• Curriculum Reinforcement Learning
• Diffusion Model
• Fuel Consumption Prediction
• Autonomous Navigation
• Sustainable Maritime Transport