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.

该项目扩展了强化学习框架如何用于网络物理系统（CPS）以实现自主性。该研究采用间歇性强化，每次执行所需的反应时不会给予奖励。这与传统的强化学习机制不同，在传统的强化学习机制中，在线训练期间为每个点提供奖励。这个框架的新颖之处在于，它可以演示当罕见事件或噪声和对抗性数据会影响这些算法的训练和性能时，如何使用强化学习。这项工作将通过与瑞典和联合王国的国际伙伴关系，在协作道路货运和协作机器人试验台上得到验证。该项目包括将高中生融入机器学习领域具有挑战性的问题中的活动，通过无人机赛车比赛来激励。这项研究的目标是通过加深学习，控制，博弈论和CPS社区之间的联系来扩展基础知识。该方法是：（i）统一工程学中关于弹性，带宽效率，鲁棒性和其他方面的新观点，这些方面无法用最先进的方法实现;（ii）为CPS开发间歇性深度学习方法，可以减轻传感器攻击，并可以处理有限的感知能力的情况;（iii）将非均衡博弈论学习与CPS中的决策，理性和信息使用从根本上不同的组件相结合;（iv）研究将学习转移到新平台的方法。该项目的教育和外联部分包括将导致技术转让的实习、特别侧重于接触代表性不足的少数群体和妇女的夏令营，该奖项反映了NSF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进行评估，被认为值得支持的搜索.