对连续时间非线性系统的数据驱动反向最佳控制
Hamed Jabbari Asl1, Anh Vu Le2, Eiji Uchibe3
1Department of Electrical Engineering, Faculty of Science and New Technology, Urmia University of Technology, Urmia, Iran.
本研究介绍了一种用于反强化学习的新算法,用于估计复杂系统中的成本函数. 它为自主系统和机器人技术提供了一种计算效率高,无模型的方法.
科学领域:
- 控制理论 控制理论
- 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:
- 估计成本函数对于理解和复制自主系统中的专家行为至关重要.
- 现有的反强化学习方法通常需要系统模型或具有高的计算成本.
研究的目的:
- 引入一种新的无模型和部分无模型算法,用于反向最佳控制.
- 用专家轨迹估计连续时间非线性确定性系统的成本函数.
主要方法:
- 该算法使用专家的输入状态轨迹.
- 它单独使用控制政策信息和Hamilton-Jacobi-Bellman方程进行参数估计.
- 在无模型版本的初始化中,需要一个单一的前向最佳控制问题解决方案.
主要成果:
- 拟议的算法有效地估计了连续时间非线性确定性系统的成本函数参数.
- 与现有方法相比,无模型方法减少了计算复杂性.
- 部分无模型版本为已知输入动态的系统提供了进一步的效率.
结论:
- 开发的算法为反强化学习提供了一个广泛适用的和计算效率高的解决方案.
- 它的有效性通过模拟得到验证,表明在自主系统和机器人技术中对现实世界的应用有潜力.
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