Optimized Reinforcement Learning Approach on Sustainable Rural Tourism Development for Economic Growth

Guofang Chen

Journal of Applied Mathematics, 2023, vol. 2023, issue 1

Abstract: A country’s economic development relies on different features such as export/import, industrial processes, and tourism. Rural tourism is a discussion‐centric research field for analyzing its contribution to a country’s economic growth. This field generates voluptuous data for tourists, expenditure, location, etc. analysis; the information increases over the years and the density of visiting tourists. Therefore, this article introduces an optimized reinforcement data analysis approach (ORDAA) for generating precise guidance information. This information is two‐faced, namely, summarized data for tourist guidance and summarized data for the country’s economic development. Data augmentation’s steep rise and downfall are analyzed using reinforcement learning, wherein decision agents are precise for a relevant summary. The relevance is identified using associated development targets over varying years. Besides, the guidance information that identifies low tourist summary or nonachievable development targets is separately identified. The identified targets are analyzed using reinforcement agents for economic growth improvements compared to the previous tourist densities. This improves the focus on rural tourism sights and economic contributions to an optimal level.

Date: 2023
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https://doi.org/10.1155/2023/4991438

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jnljam:v:2023:y:2023:i:1:n:4991438

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