对分布式参数系统的反向最佳控制与反向增强学习相结合
IEEE transactions on cybernetics
|January 13, 2026
概括
本研究介绍了对具有未知参数的系统使用逆强化学习 (IRL) 的逆最佳控制 (IOC). 人类行为学习转移最佳策略,在现实应用中增强控制性能.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 由于模型偏差,最佳控制策略可能在现实世界的分布式参数系统 (DPS) 中表现不佳.
- 预定义的奖励-权重矩阵可以导致最佳控制过程中的性能下降.
研究的目的:
- 为具有未知动态参数的DPS设计一个逆最佳控制 (IOC) 策略.
- 为应对将最佳控制政策转移到使用人类行为学习 (HBL) 的现实世界系统的挑战.
- 为了克服固定奖励-权重矩阵引起的性能退化问题.
主要方法:
- 利用人类行为学习 (HBL) 来将最佳策略从参考系统转移到现实世界的DPS.
- 在参考系统中使用了IOC的反向强化学习 (IRL) 政策代算法.
- 解决了相当的奖励重量矩阵和参考系统的最佳控制收益.
主要成果:
- 成功地将最佳控制策略转移到具有未知参数的DPS.
- 开发了一种方法来通过IRL推导奖励权重矩阵和控制收益.
- 通过模拟证明了拟议的算法的有效性和优越性.
结论:
- 拟议的IOC设计有效地处理DPS中未知的动态参数.
- HBL使最佳控制策略的可靠转移成为可能,减轻了模型偏差.
- 基于IRL的方法成功地确定了奖励功能和控制收益,提高了系统性能.
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