Abstract
Marine fishery ecosystems face unprecedented pressure from overfishing and climate variability, and these mounting threats call for management tools that go beyond traditional static quota systems. This paper puts forward an integrated framework that couples digital twin (DT) technology with deep reinforcement learning (DRL) to tackle sustainable fishery resource management. We build a five-layer hierarchical architecture whose centerpiece is a high-fidelity digital twin that mirrors fishery dynamics through explicit state-transition and observation equations rather than abstract placeholders. A Proximal Policy Optimization (PPO) agent operates within this simulated environment, receiving multidimensional state inputs—resource stocks, oceanographic conditions, fleet operations—and optimizing a composite reward function whose weights we set to ({omega }_{1}=0.3) (economic), ({omega }_{2}=0.3) (ecological), and ({omega }_{3}=0.4) (sustainability). We conduct both comparative experiments and ablation studies using East China Sea fishery data spanning 2010–2023. The ablation study, which isolates the digital twin contribution by comparing PPO with and without DT integration, confirms that the DT alone accounts for a 31.9% reward improvement. Overall, our method achieves a resource recovery index (RRI) of 0.83, outperforming traditional maximum sustainable yield management by 97.6% and standard deep Q-networks by 36.1%. Spatial heatmaps and temporal effort-control time series generated from the learned policy reveal ecologically sensible seasonal and spatial harvest patterns. This research establishes a virtual–real fusion paradigm for intelligent fishery governance and provides decision-support tools for sustainability challenges in an era of accelerating environmental change.
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Abbreviations
- DRL:
Deep reinforcement learning
- DT:
Digital twin
- MDP:
Markov decision process
- DQN:
Deep Q-network
- PPO:
Proximal policy optimization
- MSY:
Maximum sustainable yield
- AIS:
Automatic identification system
- IoT:
Internet of things
- RRI:
Resource recovery index
- EBI:
Economic benefit index
- EHI:
Ecological health index
- POE:
Path optimization efficiency
- MODIS:
Moderate resolution imaging spectroradiometer
- GEBCO:
General bathymetric chart of the oceans
- GPU:
Graphics processing unit
- KL:
Kullback-Leibler
- GAE:
Generalized advantage estimation
- SD:
Standard deviation
- VMS:
Vessel monitoring system
Funding
1. This work was supported by the Research on Optimal Path Planning Algorithm for Multi-Warehouse Collaborative Drone Scheduling Based on ABM Model (Grant No. Y202559209) from the 2025 General Scientific Research Project of the Department of Education of Zhejiang Province. 2. This work was supported by the Taking Marine Tourism as a Key Approach to Advance the Industrialization Pathway of the Chinese Ocean Civilization Source Exploration Project in Ningbo (Grant No. G2024-1-82) from the First Batch of 2024 Municipal Philosophy and Social Sciences Planning Projects (Annual Application Project). 3. This work was supported by the Significant Guiding Significance of Xi Jinping’s Cultural Thought on the Construction of Ningbo as a Modern Coastal Grand City (Grant No. WH-24-2-5) from the Second Batch of 2024 Ningbo Cultural Research Projects (Major Special Project).
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This study is based entirely on computational simulation and publicly available or institutionally shared datasets. No human subjects, animal experiments, or clinical trials were involved; therefore, ethical approval and informed consent were not required.
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Chen, Y., Ke, L. & Hu, J. Integrating digital twin technology with deep reinforcement learning for sustainable marine fishery resource management.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-55594-7
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DOI: https://doi.org/10.1038/s41598-026-55594-7
Keywords
- Digital twin
- Deep reinforcement learning
- Sustainable fishery management
- Adaptive resource management
- Proximal policy optimization
- Marine resource conservation
Source: Ecology - nature.com
