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：
1947419
负责人：
Xiangnan Zhong
金额：
$ 23.73万
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：
2019-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1947419&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.

最近的大浪潮人工智能（AI）不仅提供了巨大的进步，从基本研究到广泛的令人兴奋的应用程序，而且还带来了大量的机会以及对社区的挑战。在许多AI技术中，自适应动态编程和增强学习（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。该项目的目的是为复杂环境中的决策开发一种创新的自主层次ADP/RL方法。通过自主为提高学习和探索能力提供子目标的层次结构表示，拟议的研究为系统和适应性地开发了一种最佳的多步分层时间抽象序列提供了一种新的方法，而不是在传统方法中的单步操作。该研究方法推动了自主学习和分层控制的基础，原理，架构和算法，这将促进学习和概括决策的能力。该项目通过桥接ADP/RL和能源系统的联系，以及让学生解决尖端问题，为吸引和教育未来的专业人员提供了独特的机会。该团队由两个PI组成，这些PI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。这项研究推动了在具有高维，大数据和不确定性的复杂环境中智能决策的科学基础和方法论。与行业的合作将基本研究集成到微电网应用中，为能源部门提供了重要的技术创新。此外，开发的基于ADP/RL的智能决策方法可以使其他类型的复杂工程系统受益。此外，该项目的研究结果还有望通过培训和准备机器学习和能源系统跨学科领域的未来劳动力来满足社区的关键需求。综合外展和教育活动将为吸引妇女和少数民族进入智能系统和智能电网领域提供独特的机会。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准通过评估来获得支持的。