ASP:学习一个通用的神经解决方案!
IEEE transactions on pattern analysis and machine intelligence
|January 10, 2024
概括
我们开发了自适应式楼梯政策空间响应Oracle (ASP) 来改进机器学习解决方案,用于组合优化问题. ASP增强了跨各种问题分布和规模的概括性,大大减少了最佳性差距.
科学领域:
- 人工智能的人工智能
- 运营研究 运营研究
- 计算机科学 计算机科学
背景情况:
- 机器学习为组合优化提供了潜力,但在概括方面存在困难.
- 当问题分布或尺度发生变化时,现有的解决方案会失败.
研究的目的:
- 为组合优化问题开发一个通用的神经解答器.
- 解决基于学习的解决方案中的概括问题.
主要方法:
- 拟议的自适应式楼梯政策空间响应甲骨文 (ASP).
- 使用政策空间响应预言的嵌入式分布式探索.
- 通过课程学习实现持久规模适应.
主要成果:
- ASP在旅行销售员问题 (TSP) 和车辆路线问题 (VRP) 上表现出色.
- 实现了优化差距的显著减少:90.9% (生成的TSP) 和47.43% (现实世界的TSP).
- 优化差距减少了19% (生成的VRP) 和45.57% (现实世界的VRP).
结论:
- ASP使神经解答器能够适应看不见的分布和不同的尺度.
- 优于标准训练管道,即使训练信号较弱.
- ASP代表了在创建强大的和可扩展的神经优化解决方案方面取得的重大进展.
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