随机 LQR 的价值代与收保证
IEEE transactions on neural networks and learning systems
|April 28, 2025
概括
本研究介绍了一种无模型 (MF) 值代 (VI) 算法,用于折扣的随机线性二次调节器 (LQR) 问题,未知噪声平均值. MF VI算法有效地从系统数据中学习最佳控制策略和可行的折扣因子.
科学领域:
- 控制理论 控制理论
- 机器学习 机器学习
- 随机系统 随机系统 随机系统
背景情况:
- 线性二次调节器 (LQR) 问题是控制理论中的基本问题.
- 系统经常遇到带有未知的统计属性的噪声,使控制设计复杂化.
- 当系统动态不完全知晓时,无模型方法是可取的.
研究的目的:
- 开发一个无模型 (MF) 算法,用于折扣的随机线性二次调节器 (LQR) 问题.
- 为了解决未知平均值的附加噪声系统.
- 从数据中直接学习最佳控制政策和折扣因子.
主要方法:
- 提出了一个完全无模型 (MF) 值代 (VI) 算法.
- 该算法利用离线系统轨迹进行政策学习.
- 为学习可行的折扣因子,开发了一个单独的MF算法.
主要成果:
- 该MF VI算法汇聚到一个邻近的最佳控制政策的高概率.
- 提出的方法通过说明性示例来证明.
- 使用开发的MF方法,可以学习可行的折扣因素.
结论:
- 无模型的值代提供了一个有效的解决方案,用于降价的随机LQR问题与未知的噪声特征.
- 开发的算法为学习控制政策和数据驱动场景中的折扣因素提供了实际方法.
- 该研究强调了MF技术在复杂控制系统设计中的适用性.
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