生成高层政策模仿学习与帕雷托改进,用于节能先进的机械加工系统
IEEE transactions on neural networks and learning systems
|March 13, 2024
概括
本研究介绍了一种新的模仿学习 (IL) 方法,用于先进的机械加工系统 (AMS),以便从各种专家那里学习并优先考虑能源效率. 该方法在解决方案质量和计算时间方面优于现有技术.
科学领域:
- 制造业 工程 制造工程
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 先进的机械加工系统 (AMS) 拥有用于过程改进的智能.
- 模仿学习 (IL) 可以通过观察专家演示来利用这种智能.
- 现有的IL方法与异质的专家数据扎,并为"绿色"目标进行优化.
研究的目的:
- 开发基于IL的新三阶段政策搜索算法.
- 在AMS任务中从异质的专家政策中学习.
- 为了平衡加工性能与节能目标.
主要方法:
- 一个三阶段的政策搜索算法,集成IL,帕雷托改进学习和整体政策.
- 阶段1:高级政策学习,用于加工基础知识和多样化的决策.
- 第2阶段:在政策多重体上进行巴雷托改进学习以节能.
- 第三阶段:整合政策和人类反放大.
主要成果:
- 拟议的IL方法成功地学习了异构的专家政策.
- 该算法有效地平衡了加工性能与能源效率.
- 实验结果显示出优越的解决方案质量和比元启发学和基线方法更快的计算时间.
结论:
- 新的IL方法通过向各种专家学习和优化能源效率来增强AMS的能力.
- 该方法通过处理各种专家数据并纳入绿色制造目标来解决当前IL的局限性.
- 拟议的算法为智能加工系统提供了重大进步.
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