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CRII: CPS: A Self-Learning Intelligent Control Framework for Networked Cyber-Physical Systems

CRII：CPS：网络信息物理系统的自学习智能控制框架

基本信息

批准号：
1947418
负责人：
Xiangnan Zhong
金额：
$ 17.44万
依托单位：
Florida Atlantic University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-19 至 2023-02-28
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1947418&HistoricalAwards=false
关键词：
CRII CPS Self Learning Intelligent

项目摘要

The goal of this project is to addresses challenges in machine learning for intelligent physical systems that interact with one another. The approach is to explore Reinforcement Learning (RL) strategies, where systems are rewarded when behaving correctly, for interacting physical systems when the systems with which they interact may react in inconsistent ways. The results are expected to contribute to a new self-learning intelligent control framework, where the systems under design can decide how to interact with their inconsistent neighbors in a way that will improve how they learn. This will advance reinforcement learning for networked cyber-physical systems (CPS) which can have emergent behaviors when they interact (for example, unmanned aerial systems) and are frequently inconsistent due to uncertainties in their distributed nature (for example, the smart grid).Three major fundamental contributions to the scientific field are expected. First, a new distributed RL algorithm will learn suitable reward functions automatically without requiring external supervisions. This work will relax human efforts and scale RL algorithms to more complex environment. Second, novel transfer learning-based RL architectures will be designed by reusing past knowledge from multiple sources. This design will further accelerate learning process in networked CPS. Third, this proposed method will be implemented on a multi-robot testbed to advance the learning in robot applications. Outreach and dissemination plans cultivate the scientific curiosity of K-12 students, and students from underrepresented groups, and motivate their interests in Science, Technology, Engineering, and Math (STEM) programs. Furthermore, the integration of the project's cutting-edge research results into new courses will aid retention of current STEM students.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.

该项目的目标是解决机器学习中相互交互的智能物理系统的挑战。该方法是探索强化学习（RL）策略，当系统行为正确时，系统会得到奖励，当与它们交互的系统可能以不一致的方式做出反应时，系统会与物理系统交互。研究结果有望为一个新的自学习智能控制框架做出贡献，在这个框架中，设计中的系统可以决定如何与不一致的邻居进行交互，从而改善它们的学习方式。这将促进网络化信息物理系统（CPS）的强化学习，这些系统在交互时可能会出现紧急行为（例如，无人机系统），并且由于其分布式性质的不确定性而经常不一致（例如，智能电网）。首先，一种新的分布式RL算法将自动学习合适的奖励函数，而无需外部监督。这项工作将放松人类的努力，并将RL算法扩展到更复杂的环境。其次，将通过重用来自多个来源的过去知识来设计新的基于迁移学习的RL架构。该设计将进一步加快学习过程中的网络CPS。第三，该方法将在多机器人测试平台上实现，以推进机器人应用中的学习。推广和传播计划培养K-12学生的科学好奇心，以及来自代表性不足群体的学生，并激发他们对科学，技术，工程和数学（STEM）课程的兴趣。此外，将项目的前沿研究成果融入新课程将有助于留住现有的STEM学生。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。