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

该网络物理系统（CPS）项目将取得基础方法上的进步，使基于强化学习（RL）的控制算法解决方案成为可能，这些解决方案是由智能交通信号控制系统中的问题驱动的。计算、通信、存储和传感方面的最新进展导致了对现代网络物理系统（cps）（如智能交通系统）中基于数据驱动学习的决策和控制的需求。在这样的系统中，决策代理需要在复杂的环境中以安全可靠的方式运行，同时需要尊重约束条件。该项目将通过将智能交通系统中的交通信号控制系统作为我们的激励CPS应用和评估平台，在稳健的RL解决方案和安全和受限的RL方面取得基础进展，并提供可证明的保证。这项工作还将侧重于推进课程开发，从代表性不足的群体招募学生，让本科生参与研究，K-12外展，以及通过研讨会、会议和研讨会进行研究社区外展。研究人员将与公司和其他利益相关者沟通研究结果，并为他们提供有关方法论的教育材料。技术途径包括：1.技术途径；稳健的RL解决方案，结合模型类知识，使用未来预测和鲁棒性特征，以及非策略方法来解决由于使用模拟器/模拟器或离线数据而引起的分布变化和数据缺乏；和2。使用无模型方法和函数近似方法的高效、安全和受限的强化学习算法，以及部分观察系统的方法。为了与激励CPS应用闭环，RL算法将在交通信号控制的背景下进行评估，通过使用两个仪器站点的模型进行基于综合模拟的评估。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.