概括
本研究引入了折扣逆强化学习 (DIRL) 方法,以解决具有挑战性的线性二次控制问题,包括跟踪,即使是未知的系统动态和值函数.
科学领域:
- 控制理论 控制理论
- 机器学习 机器学习
- 强化学习是一种强化学习.
背景情况:
- 具有未知的动态和值函数的线性二次控制是一个重大挑战.
- 现有的方法主要解决监管问题,但与跟踪问题作斗争.
研究的目的:
- 开发一种独立于模型的方法,用于解决线性二次方程调节和跟踪问题.
- 解决具有未知值函数和动态的连续时间系统.
主要方法:
- 建议采用折扣式反向强化学习 (DIRL) 方法.
- 使用准牛顿算法构建和最小化一个错误度量,以恢复值函数和控制增益.
- 介绍了三个DIRL算法:基于模型的,没有模型的,政策之外的和没有模型的政策.
主要成果:
- 拟议的DIRL方法有效地处理线性二次调节和跟踪问题.
- 无模型算法利用专家演示或在线数据,而没有先前的系统知识.
- 解决方案的稳定性,收性和存在条件被严格分析.
结论:
- 开发的DIRL框架为未知系统属性的线性二次控制提供了强大的解决方案.
- 数字模拟验证了理论发现,并证明了该方法的有效性.
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