数据驱动的反向增强学习,用于异质的最佳稳固形成控制
IEEE transactions on cybernetics
|March 14, 2025
概括
本研究介绍了针对多代理系统 (MAS) 的数据驱动反增强学习 (IRL) 算法,以实现最佳的形成控制,尽管存在干扰. 这些方法确保了复杂的MAS任务的稳定性和趋同性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 控制系统 控制系统
背景情况:
- 多代理系统 (MAS) 中的异质形成控制由于复杂的动态和外部干扰而带来了挑战.
- 识别未知的奖励函数和最佳控制策略对于MAS性能至关重要.
- 现有的方法通常需要对系统动态有充分的了解,这限制了它们的适用性.
研究的目的:
- 开发新的数据驱动的反增强学习 (IRL) 算法,以实现MAS中最佳的形成控制.
- 在干扰条件下解决异质的形成控制问题.
- 为了使MAS能够从专家演示中学习最佳行为,而无需事先了解其动态.
主要方法:
- 拟议的专家-估计者-学习者MAS架构与相似的交互图.
- 为估计器MAS开发了一个基于模型的IRL算法,以获得最佳的控制和奖励功能.
- 为学习者MAS引入了一个强大的无模型IRL算法,利用估计器结果而不需要学习者动态.
- 介绍了拟议的IRL算法的数据驱动实现.
主要成果:
- 成功重建了评估者和学习者MAS的最佳控制和奖励功能.
- 通过演示,证明了识别未知的奖励函数和最佳控制的能力.
- 通过理论分析验证了多剂系统的稳定性和趋同性.
- 通过模拟结果确认了拟议的算法的有效性.
结论:
- 开发的数据驱动的IRL算法有效地解决了在干扰下MAS的异质形成控制.
- 专家 - 估计者 - 学习者框架使得学习最佳政策,即使没有完全了解系统动态.
- 这项研究为奖励函数识别和MAS的最佳控制提供了强大的方法.
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