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Collaborative Research: Autonomous Hierarchical Adaptive Dynamic Programming for Decision Making in Complex Environment

协作研究：复杂环境下自主分层自适应动态规划决策

基本信息

批准号：
1917275
负责人：
Haibo He
金额：
$ 22.27万
依托单位：
University of Rhode Island
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：
2019-08-01 至 2023-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1917275&HistoricalAwards=false
关键词：
Collaborative Research Autonomous Hierarchical Adaptive

项目摘要

The recent big wave of artificial intelligence (AI) not only provided tremendous advancements ranging from fundamental research to a wide range of exciting applications, but also presents enormous amounts of opportunities as well as challenges to the community. Among many of the AI techniques, adaptive dynamic programming and reinforcement learning (ADP/RL) is widely considered as one of the key methodologies for learning-based intelligent decision-making process.The objective of this project is to develop an innovative autonomous hierarchical ADP/RL approach for decision making in complex environments. By autonomously providing a hierarchical representation of sub-goals for improved learning and exploration capability, the proposed research provides a new approach to systematically and adaptively develop an optimal multi-step hierarchical temporal abstraction sequence, rather than the one-step primitive action in traditional methods. The research method advances the foundations, principles, architectures, and algorithms for autonomous learning and hierarchical control, which will facilitate the capability of learning and generalization for decision-making. This project provides unique opportunities to attract and educate future professionals by bridging the connections of ADP/RL and energy systems, and for students to work on cutting-edge problems. The team consists of two PIs with strong collaborations and complementary expertise in computational intelligence, machine learning, autonomous control, and the smart grid.This research advances the scientific foundations and methodologies of intelligent decision making in complex environments with high-dimensionality, big data, and uncertainty. The collaborations with industry integrates fundamental research into a microgrid application providing critical technical innovations to the energy sector. In addition, the developed ADP/RL based intelligent decision making method can benefit other types of complex engineering systems. Furthermore, the research results of this project are also expected to fulfill a critical need in the community by training and preparing future workforce in the cross-disciplinary areas of machine learning and energy systems. The integrative outreach and education activities will provide unique opportunities to attract women and minorities into the intelligent system and smart grid field.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.

最近的人工智能（AI）浪潮不仅提供了从基础研究到广泛的令人兴奋的应用的巨大进步，而且也为社区带来了巨大的机遇和挑战。在众多的人工智能技术中，自适应动态规划和强化学习（ADP/RL）被广泛认为是基于学习的智能决策过程的关键方法之一，本项目的目标是开发一种创新的自主分层ADP/RL方法来解决复杂环境下的决策问题。通过自主地提供子目标的分层表示以提高学习和探索能力，所提出的研究提供了一种新的方法来系统地和自适应地开发最优的多步分层时间抽象序列，而不是传统方法中的一步原始动作。该研究方法提出了自主学习和递阶控制的基础、原理、结构和算法，这将有助于提高决策的学习能力和泛化能力。该项目提供了独特的机会，通过桥接ADP/RL和能源系统的连接来吸引和教育未来的专业人士，并让学生研究前沿问题。该研究团队由两名PI组成，他们在计算智能、机器学习、自主控制和智能电网方面具有强大的合作和互补的专业知识，该研究推进了在高维、大数据和不确定性的复杂环境中进行智能决策的科学基础和方法。与工业界的合作将基础研究整合到微电网应用中，为能源部门提供关键的技术创新。此外，所开发的基于ADP/RL的智能决策方法也可用于其他类型的复杂工程系统。此外，该项目的研究成果还有望通过在机器学习和能源系统的跨学科领域培训和准备未来的劳动力来满足社区的关键需求。综合推广和教育活动将提供独特的机会，吸引妇女和少数民族进入智能系统和智能电网领域。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标准进行评估，被认为值得支持。