What question did this study set out to answer?

The aim is to model aquaculture production and governance optimization using hybrid deep reinforcement learning for improved sustainability.

June 3, 2026

Sustainable Aquaculture Transition: Intelligent Policy Optimisation and Simulation Using Hybrid Deep Reinforcement Learning

Key Points

The aim is to model aquaculture production and governance optimization using hybrid deep reinforcement learning for improved sustainability.
Used a Hybrid Deep Reinforcement Learning framework integrated with feature engineering and multi-policy simulation.
Datasets sourced from FAO, Kaggle, and regional aquaculture covering 1960 to 2018.
Implemented principal component analysis and feature selection for data analysis.
HDRL-based policy simulation achieved a mean reward about 15% higher than baseline strategies.
Produced nearly a 12% reduction in production volatility.
Identified water quality and energy optimization as core factors for sustainability.

Abstract

Introduction: The world relies on aquaculture to help meet the increasing demand for seafood without compromising wild fish stocks. Over the last fifty years, the shift from capture fisheries to aquaculture has been one of the most momentous transformations in the world's food systems. Materials and Methods: The study used a Hybrid Deep Reinforcement Learning (HDRL) framework, integrated with feature engineering and multi-policy simulation, to model aquaculture production, sustainability, and governance optimisation. These datasets were obtained from the FAO, Kaggle, and regional aquaculture sources covering the period 1960 to 2018. Results: HDRL-based policy simulation outperforms the baseline, and the convergence of policy rewards indicates sustainability and increased production efficiency. Analysis that included both principal component analysis and feature selection identified the resource input with water- quality and energy optimisation as the core explanatory factor. Discussion: With historical aquaculture information, the proposed model achieves a mean reward about 15% higher than the baseline policy strategies and nearly a 12% lower production volatility. According to these findings, aquaculture governance can become more adaptive and sustainability-aware via reinforcement learning. conclusion: The HDRL table builds on a data-driven strategy to promote aquaculture policy transitions. Sustainable goals can be achieved through technological and political measures to balance nature and government. The future blue economy projects will be extended to multinational cooperation. Each nation agrees to its role, the whole project comes together, and it ends up best for every country, not just a few. Conclusion: The HDRL mechanism proposed here can enhance aquaculture governance to meet production targets while respecting environmental constraints. This paper presents a replicable model for sustainable food systems that uses artificial intelligence-based optimisation, feature learning, and policy simulation.

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