基于神经网络的加速安全Q学习,以优化对具有状态约束的离散时间非线性系统的最佳控制
Mingming Zhao1, Ding Wang1, Junfei Qiao1
1School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China; Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China; Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing 100124, China; Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing 100124, China.
本研究介绍了一种加快安全的Q学习 (SQL) 方法,用于具有状态约束的非线性系统. 它有效地确保了安全性和最佳性,以更少的代实现更快的融合.
科学领域:
- 控制理论 控制理论
- 机器学习 机器学习
- 优化优化 优化优化
背景情况:
- 传统的Q学习在状态约束下对非线性系统进行安全的最佳控制方面扎.
- 实现并发的安全性和最佳性是控制工程的一个重大挑战.
研究的目的:
- 为具有状态约束的离散时间非线性系统提出加速安全的Q学习 (SQL) 技术.
- 将安全约束和最佳性整合到一个统一的框架中,以加强控制.
- 提高安全强化学习算法的学习速度和融合.
主要方法:
- 设计了一个可调节的控制屏障功能,集成到成本功能中,以处理约束.
- 使用安全离线数据采用非政策值代SQL方法.
- 整合了历史代信息和Nesterov Momentum以加快政策评估和改进.
主要成果:
- 加快的SQL算法证明了趋同,最佳性和安全性.
- 模拟显示了该方法在具有状态约束的非线性系统中的有效性.
- 这种方法需要更少的代,并实现更快的趋同到平衡点.
结论:
- 建议的加速SQL技术有效地解决了受约束的非线性系统的安全最佳控制.
- 这种方法可以显著提高学习速度和控制性能.
- 它为需要安全和高效控制的现实应用提供了一个强大的框架.
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