Deep Reinforcement Learning for Risk and Disaster Management in Energy-Efficient Marine Ranching

Gelian Song, Meijuan Xia and Dahai Zhang ()
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Gelian Song: School of Modern Information Technology, Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou 310053, China
Meijuan Xia: Ocean College, Zhejiang University, Zhoushan 316021, China
Dahai Zhang: Ocean College, Zhejiang University, Zhoushan 316021, China

Energies, 2023, vol. 16, issue 16, 1-20

Abstract: The marine ranching industry in China is transitioning from traditional farming to a digital and intelligent model. The use of new technologies, algorithms, and models in the era of artificial intelligence (AI) is a key focus to enhance the efficiency, sustainability, and resilience of marine ranch operations, particularly in risk and disaster management. This study proposes a methodology for applying deep reinforcement learning to decision making in this domain. The approach involves creating an environmental model based on decision objects and scenarios, determining the number of decision makers, and selecting a single or multi-agent reinforcement learning algorithm to optimize decision making in response to randomly generated disasters. Three core innovations are presented: the development of a disaster simulator for marine ranching scenarios, the application of reinforcement learning algorithms to address risk and disaster management problems in marine ranching. Future research could focus on further refining the methodology by integrating different data sources and sensors and evaluating the social and economic impacts of AI-driven marine ranching. Overall, this study provides a foundation for further research in this area, which is expected to play an increasingly important role in global food production, environmental sustainability, and energy efficiency.

Keywords: marine ranching; artificial intelligence; deep reinforcement learning; risk and disaster management; environmental modeling; decision-making optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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