CAREER: Towards an Intermittent Learning Framework for Smart and Efficient Cyber-Physical Autonomy

职业：走向智能高效的网络物理自治的间歇性学习框架

• 批准号: 1750789
• 负责人: Kyriakos G Vamvoudakis
• 金额: $ 50万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-01 至 2018-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1750789&HistoricalAwards=false
• 关键词: CAREER; Towards; Intermittent; Learning; Framework

This project expands how reinforcement learning frameworks can be used for Cyber-Physical Systems (CPS) for autonomy. The research utilizes intermittent reinforcement, where a reward is not given every time the desired response is performed. This differs from traditional reinforcement learning mechanisms, in which a reward is given for each point during online training. What is novel in this framework is that it can demonstrate how reinforcement learning can be used when rare events, or noisy and adversarial data, can affect the training and performance of these algorithms. The work will be validated on collaborative road freight transport and collaborative robotics testbeds, through international partnerships with Sweden and the United Kingdom. The project includes activities that integrate high-school students into challenging problems in machine learning areas, motivated through drone racing competitions.The goal of this research is to expand foundational knowledge through deepened ties between the learning, control, game theory, and CPS communities. The approach is to, (i) unify new perspectives of learning in engineering with respect to resiliency, bandwidth efficiency, robustness, and other aspects that cannot be achieved with the state-of-the-art approaches; (ii) develop intermittent deep learning methods for CPS that can mitigate sensor attacks and can handle cases of limited sensing capabilities; (iii) incorporate nonequilibrium game-theoretic learning in CPS with components whose decision-making, rationality, and information usage are fundamentally different; and (iv) investigate ways to transfer learning to new platforms. The project's education and outreach component includes internships that will lead to technology transfer, summer camps with a special focus on reaching out to underrepresented minorities and women, and collaboration with institutions in Sweden and the United Kingdom through student exchange programs.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.

该项目扩展了如何将增强学习框架用于自治的网络物理系统（CPS）。该研究利用间歇性加强，每次执行所需响应时都不会获得奖励。这与传统的增强学习机制不同，在线培训期间，每个点都会给予奖励。在此框架中，新颖的是，它可以证明在罕见事件，嘈杂和对抗性数据时如何使用增强学习可以影响这些算法的训练和性能。这项工作将通过与瑞典和英国的国际合作伙伴关系在协作道路运输和协作机器人测试床上进行验证。该项目包括将高中生整合到机器学习领域中具有挑战性问题的活动，这是通过无人机赛车竞争的动机。这项研究的目的是通过加深的学习，控制，游戏理论和CPS社区之间的加深联系来扩展基本知识。该方法是，（i）统​​一工程学学习的新观点，方面的弹性，带宽效率，鲁棒性以及其他最先进方法无法实现的方面； （ii）为CP开发间歇性的深度学习方法，以减轻传感器攻击并可以处理有限的传感能力案例； （iii）将CP中的非平衡游戏理论学习与决策，合理性和信息使用根本不同的组成部分结合在一起； （iv）研究将学习转移到新平台的方法。该项目的教育和外向组成部分包括实习，这些实习将导致技术转移，特别关注代表性不足的少数群体和妇女的夏令营，并通过学生交流计划与瑞典和英国的机构合作。该奖项反映了NSF的法定任务，并通过评估基金会的智能效果和广泛的范围来表现出值得的支持，并具有值得的评估。