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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

基本信息

批准号：
1851588
负责人：
Kyriakos G Vamvoudakis
金额：
$ 49.93万
依托单位：
Georgia Tech Research Corporation
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：
2018-08-01 至 2025-04-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1851588&HistoricalAwards=false
关键词：
CAREER Towards Intermittent Learning Framework

项目摘要

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）的自治。该研究采用间歇性强化，即不会在每次执行所需响应时都给予奖励。这与传统的强化学习机制不同，传统的强化学习机制在在线训练期间对每个点给予奖励。该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可能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英国的国际合作，在协作道路货运和协作机器人测试平台上得到验证。该项目包括通过无人机竞赛竞赛激励高中生解决机器学习领域挑战性问题的活动。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。该方法旨在：(i) 统一工程学习的新视角，涉及弹性、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 CPS 开发间歇性深度学习方法，可以减轻传感器攻击并处理传感能力有限的情况； (iii) 将非平衡博弈论学习纳入 CPS 中，其决策、理性和信息使用完全不同； (iv) 研究将学习转移到新平台的方法。该项目的教育和推广部分包括导致技术转让的实习、特别注重接触代表性不足的少数族裔和妇女的夏令营，以及通过学生交换计划与瑞典和英国的机构合作。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。

项目成果

期刊论文数量（80）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Hamiltonian-Driven Hybrid Adaptive Dynamic Programming

哈密顿驱动的混合自适应动态规划

DOI：
10.1109/tsmc.2019.2962103
发表时间：
2020-01
期刊：
IEEE Transactions on Systems, Man, and Cybernetics: Systems
影响因子：
0
作者：
Yongliang Yang;Kyriakos G. Vamvoudakis;Hamidreza Modares;Yixin Yin;Donald C. Wunsch
通讯作者：
Donald C. Wunsch

A Modular Approach to Verification of Learning Components in Cyber-Physical Systems

网络物理系统中学习组件验证的模块化方法

DOI：
10.2514/6.2023-0131
发表时间：
2023
期刊：
Proc. AIAA SCITECH 2023 Forum
影响因子：
0
作者：
Zhai, Lijing;Kanellopoulos, Aris;Fotiadis, Filippos;Vamvoudakis, Kyriakos G.;Hugues, Jérôme
通讯作者：
Hugues, Jérôme

Synchronous Reinforcement Learning-Based Control for Cognitive Autonomy

基于同步强化学习的认知自主控制

DOI：
10.1561/2600000022
发表时间：
2020
期刊：
Foundations and Trends® in Systems and Control
影响因子：
0
作者：
Vamvoudakis, Kyriakos G.;Kokolakis, Nick-Marios T.
通讯作者：
Kokolakis, Nick-Marios T.

Dynamic Intermittent Feedback Design for H∞ Containment Control on a Directed Graph

有向图上 H 遏制控制的动态间歇反馈设计

DOI：
10.1109/tcyb.2019.2933736
发表时间：
2020
期刊：
IEEE Transactions on Cybernetics
影响因子：
11.8
作者：
Yongliang Yang;Hamidreza Modares;Kyriakos G Vamvoudakis;Yixin Yin;Donald C Wunsch
通讯作者：
Donald C Wunsch

Switching for Unpredictability: A Proactive Defense Control Approach

针对不可预测性进行切换：主动防御控制方法