一个集成RAG-LLM和深度Q网络的框架,用于智能鱼类控制系统
Pobporn Danvirutai1, Siripavee Charoenwattanasak2, Siriporn Tola2
1College of Computing, Khon Kaen University, Khon Kaen, Thailand.
Scientific reports
|July 2, 2025
概括
本研究介绍了一种人工智能系统,该系统结合了Retrieval-Augmented Generation Large Language Models (RAG-LLM) 和Deep Q-Networks (DQN) 进行自主养鱼. 混合人工智能方法提高了水产养殖生产的效率和可持续性.
科学领域:
- 水产养殖是水产养殖的一种方式.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 养鱼行业正在寻求技术解决方案,以提高效率,生产力和可持续性.
- 目前的水产养殖管理往往依赖于专家知识,限制了可扩展性和可访问性.
研究的目的:
- 调查采用检索增强代大型语言模型 (RAG-LLM) 与自主水产养殖深度Q网络 (DQN) 的集成.
- 将这种混合人工智能系统的性能与传统方法和其他人工智能系统进行比较.
主要方法:
- 开发了一个自主系统,使用RAG-LLM和DQN的集体学习.
- 整合物联网 (IoT) 设备用于全面监测养,疾病,生长和水质.
- 利用多数投票来制定政策,并预先培训了LLM知识,以改善学习趋同.
主要成果:
- 与其他方法相比,RAG-LLM和DQN混合方法显示出较高的鱼类生长率.
- 实现了自动化政策的快速稳定,表明有效的系统控制.
- 该系统成功地利用了预先训练的LLM知识来加速学习.
结论:
- 集成的RAG-LLM和DQN系统为自主水产养殖提供了一个有前途的解决方案.
- 这项技术有可能使非专家在管理渔场和扩大生产方面有能力.
- 该研究强调了一条通过水产养殖中先进的人工智能提高全球粮食可持续性的途径.
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