基于梯度的框架,用于黑子函数的双级优化:协同利用无模型的强化学习和隐含的函数差异化
1Department of Chemical and Biomolecular Engineering, University of California, Berkeley, California 94720, United States.
概括
本研究引入了一种基于梯度的新型框架,用于解决复杂的双级优化问题,即使客观函数信息有限. 它允许对不确定的系统进行可扩展,高效的控制政策学习.
科学领域:
- 优化优化 优化优化
- 机器学习 机器学习
- 控制理论 控制理论
背景情况:
- 由于复杂的变量相互作用,双级优化问题存在重大挑战.
- 现有的方法往往过于简化或缺乏可扩展性,用于高维,非凸的问题.
- 基于梯度的方法受到隐性变量关系和可区分性问题的阻碍.
研究的目的:
- 开发一个基于梯度的框架,以实现双层优化,并实现黑盒目标.
- 为高维的双层问题提供可扩展的解决方案.
- 解决在双层优化中区分隐性关系的挑战.
主要方法:
- 利用隐式函数定理进行梯度计算.
- 使用无模型的强化学习 (RL) 进行基于梯度的更新.
- 利用政策梯度RL进行可扩展,高维的更新.
主要成果:
- 该框架通过隐式差异化成功计算了上层目标梯度.
- 政策梯度RL为高级别决策提供可扩展的梯度更新.
- 对于不确定的系统,学习模型预测控制政策的有效性已被证明.
结论:
- 拟议的框架为黑子目标的双层问题提供了可扩展和高效的解决方案.
- 协同实现无衍生品优化和隐性差异化,以提高性能.
- 为复杂的优化任务开辟了新的研究途径.
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