CPS: Medium: Collaborative Research: Developing Data-driven Robustness and Safety from Single Agent Settings to Stochastic Dynamic Teams: Theory and Applications

CPS：中：协作研究：从单代理设置到随机动态团队开发数据驱动的鲁棒性和安全性：理论与应用

• 批准号: 2240982
• 负责人: Debankur Mukherjee
• 金额: $ 32万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2240982&HistoricalAwards=false
• 关键词: CPS; Medium; Collaborative; Research; Developing

This Cyber-Physical Systems (CPS) project will make foundational methodological advances that enable safe and robust reinforcement learning (RL)-based control algorithmic solutions that are driven by problems in smart traffic signal control systems. Recent advances in computation, communication, storage, and sensing have led to a demand for data-driven learning-based decision-making and control in modern cyber-physical systems (CPSs), such as smart transportation systems. In such systems, decision-making agents need to operate safely and in a robust manner while working in complex environments with constraints that need to be respected. This project will develop foundational advances in robust RL solutions, and safe and constrained RL with provable guarantees by taking traffic signal control systems within smart transportation systems as our motivating CPS application and evaluation platform. This work will additionally focus on advancing curriculum development, recruitment of students from under-represented groups, involvement of undergraduate students in research, K-12 outreach, and also research community outreach via workshops, conference sessions, and seminars. The researchers will interface with companies and other stakeholders to communicate the results of the research as well as provide them with educational material on methodology. The technical approaches include: 1. Robust RL solutions incorporating model class knowledge, use of future predictions and robustness characterizations, and off-policy methods to address distributional shifts and data paucity arising from the use of a simulator/emulator or offline data; and 2. Efficient, safe, and constrained RL algorithms using model-free approaches and function-approximated methods, and also methods for partially-observed systems. To close the loop with the motivating CPS application, the RL algorithms will be evaluated in the context of traffic signal control via a comprehensive simulation-based evaluation using models of two instrumented sites.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该网络物理系统（CPS）项目将使基础方法学进步能够实现安全，强大的增强学习（RL）基于控制的控制算法解决方案，这些解决方案是由智能交通信号控制系统中问题驱动的。计算，通信，存储和感知的最新进展已导致对基于数据驱动的学习的决策和控制的需求，例如智能运输系统等现代网络物理系统（CPSS）。在这样的系统中，决策代理需要在需要受到尊重的限制的复杂环境中工作，在复杂的环境中工作。该项目将在强大的RL解决方案中发展基本进步，并通过将交通信号控制系统在智能运输系统中作为我们激励的CPS应用程序和评估平台，并获得可证明的保证和可证明的保证。 这项工作还将着重于推进课程开发，从代表性不足的群体中招募学生，本科生参与研究，K-12外展活动，以及通过研讨会，会议课程和研讨会的研究社区外展活动。研究人员将与公司和其他利益相关者进行交流，以传达研究结果，并为他们提供有关方法论的教育材料。技术方法包括：1。结合模型类知识的鲁棒RL解决方案，使用未来的预测和鲁棒性特征以及通过使用模拟器/emulator或离线数据引起的分布变化和数据匮乏的非政策方法；和2。使用无模型方法和功能评估方法的有效，安全和约束的RL算法，以及针对部分观察到的系统的方法。为了通过激励CPS应用程序结束循环，将通过使用两个仪器站点的模型进行基于综合的基于仿真的评估来评估RL算法。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力和更广泛影响的评估来通过评估来获得支持的值得审查。