对具有时间逻辑规范的黑盒动态系统进行数据驱动的安全策略优化
IEEE transactions on neural networks and learning systems
|December 18, 2023
概括
数据驱动的安全策略优化 (D2SPO) 能够在黑子系统中进行复杂任务的强化学习 (RL). 这种方法通过控制屏障功能 (CBF) 确保安全,并实现高任务完成率.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制理论 控制理论
- 人工智能的人工智能
背景情况:
- 基于学习的政策优化显示出对通用控制系统的前景.
- 现有的方法在实现复杂的目标和在学习和执行过程中确保黑子系统的安全性方面面临挑战.
研究的目的:
- 开发一种基于强化学习 (RL) 的新方法,数据驱动的安全政策优化 (D2SPO),用于黑子系统的安全政策改进.
- 共同学习用于系统安全的控制障碍函数 (CBF) 和用于复杂任务目标的线性时间逻辑 (LTL) 引导的RL算法.
主要方法:
- 通过重新设计数据集和损失函数,D2SPO学习了用于黑盒动态系统的可证明安全的CBF.
- 制定一个以LTL为指导的RL策略,以高效地完成具有LTL目标的任务,利用LTL在表示任务进展的能力.
主要成果:
- 与最先进的基线相比,D2SPO在数值和实验研究中表现出更高的性能.
- 该方法实现了超过95%的安全率和近100%的任务完成率.
结论:
- D2SPO有效地解决了实现复杂任务目标的挑战,同时确保黑子系统中的政策安全.
- 开发的方法为复杂的控制应用中安全和高效的强化学习提供了强大的解决方案.
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