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Updated: Jan 25, 2026

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线性连续时间系统的输出反控制使用折扣反向增强学习
IEEE transactions on cybernetics
|January 23, 2026
概括
本研究引入了一种新的折扣逆强化学习 (DIRL) 算法,用于仅使用输出数据控制未知系统. 该方法有效地重建状态并学习最佳控制政策,优于现有技术.
科学领域:
- 控制系统工程 控制系统工程
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 折扣逆增强学习 (DIRL) 通常需要全态反,限制其在现实应用中仅使用输入输出数据的使用.
- 具有部分可观测状态的未知连续时间 (CT) 系统存在重大控制挑战.
- 学习未知的折扣价值函数对于最佳的控制政策推导至关重要.
研究的目的:
- 开发一种新的无模型,输出反 (OPFB) DIRL算法,用于对未知的CT系统进行线性二次控制.
- 通过从输入-输出数据中学习,解决现有的DIRL方法的局限性.
- 用专家控制输出数据重建系统状态,用于政策学习.
主要方法:
- 使用专家控制和测量输出数据设计了一种状态重建方法.
- 介绍了一个无模型的OPFB DIRL算法,以代学习未知值函数和最佳控制策略.
- 对算法融合和解决方案独特性进行了严格的分析.
主要成果:
- 拟议的算法有效地恢复了专家控制政策.
- 与最先进的方法相比,模拟显示出更高的计算效率.
- 该算法成功地处理了部分可观测的状态和未知值函数.
结论:
- 新的OPFB DIRL算法为控制具有有限状态信息的未知CT系统提供了有效的解决方案.
- 该方法通过仅使用输入-输出数据,提高了DIRL在实际场景中的适用性.
- 该算法提供了一种计算效率高和强大的方法来学习最佳控制策略.
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