CAREER: Toward Artificial General Intelligence for Complex Adaptive Systems: A Natural Concurrent “Learning-in-Learning” Control Paradigm

职业：走向复杂自适应系统的通用人工智能：自然并发“学习中学习”控制范式

• 批准号: 2047064
• 负责人: Zhen Ni
• 金额: $ 50.01万
• 依托单位: Florida Atlantic University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-15 至 2026-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2047064&HistoricalAwards=false
• 关键词: CAREER; Toward; Artificial; General; Intelligence

Artificial intelligence (AI) technologies are transforming nearly every aspect of our lives and reinforcement learning (RL) is viewed as one of next big research topics in the current AI wave. While the existing AI and RL achievements are exciting, the fundamental research of data aggregation, learning and approximation capability, and the performance generalization during uncertainties, is not fully yet developed. There is still a gap from the current state-of-the-art techniques to the artificial general intelligence that can bring good performance in learning speed, data efficiency, and generalization of the optimization performance.Inspired by this observation, the PI proposes a natural concurrent RL framework that carries three major advantages over traditional RL methods, namely the i) advantages of simultaneously learning multimodal properties of the complex system; ii) structural advantages of using a personalized learning scheme; and iii) implementation advantages of the data-driven sample-efficient design. Within this framework, the PI proposes to design two concurrent RL methods to consolidate past experiences and anticipatory knowledge and build the “learning-in-learning” control paradigm. The theoretical results will certify that the proposed RL framework can be deployed with high confidence for complex adaptive systems under uncertain environments. The applications on smart energy community will support the novel learning framework and theoretical results.Beyond the scientific impacts, the proposed research has broader impacts for a wide range of research disciplines including transportation, rehabilitation, and robotics. The integration of research and education activities will also positively impact the institutions regionally and nationally. A proposed workshop will bring world renown experts to engage (state college) students and young researchers with limited financial supports to attend professional conferences. The collaboration with the industry and the national laboratory provides the students the opportunity to get external training, which can lead to competitive job offers. The proposed take-home AI/RL projects will promote interactive distance learning for schools with limited research capacity (e.g., rural community college) and for students with the preference of remote studying during the current pandemic. These activities will vigorously contribute to the nation’s AI workforce development.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.

人工智能 (AI) 技术正在改变我们生活的几乎各个方面，强化学习 (RL) 被视为当前人工智能浪潮中的下一个重大研究主题之一。虽然现有的人工智能和强化学习成果令人兴奋，但数据聚合、学习和逼近能力以及不确定性下的性能泛化的基础研究尚未完全发展。与当前最先进的技术相比，能够在学习速度、数据效率和优化性能泛化方面带来良好表现的通用人工智能仍然存在差距。受这一观察的启发，PI 提出了一种自然并发 RL 框架，它比传统 RL 方法具有三大优势，即 i）同时学习复杂系统的多模态属性的优势； ii) 使用个性化学习方案的结构优势； iii) 数据驱动的样本高效设计的实施优势。在此框架内，PI建议设计两种并行的强化学习方法，以巩固过去的经验和预期知识，并构建“学习中学习”的控制范式。理论结果将证明所提出的强化学习框架可以在不确定环境下以高置信度部署于复杂的自适应系统。智能能源社区的应用将支持新颖的学习框架和理论成果。除了科学影响之外，拟议的研究还对包括交通、康复和机器人技术在内的广泛研究学科产生更广泛的影响。研究和教育活动的整合也将对地区和国家的机构产生积极影响。拟议的研讨会将邀请世界知名专家邀请（州立大学）学生和经济支持有限的年轻研究人员参加专业会议。与行业和国家实验室的合作为学生提供了接受外部培训的机会，从而获得有竞争力的工作机会。拟议的带回家的人工智能/强化学习项目将促进研究能力有限的学校（例如农村社区学院）和在当前大流行期间偏爱远程学习的学生进行交互式远程学习。这些活动将为国家人工智能劳动力发展做出积极贡献。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

A Modified Maximum Entropy Inverse Reinforcement Learning Approach for Microgrid Energy Scheduling

一种改进的微电网能量调度最大熵逆强化学习方法

• DOI: 10.1109/pesgm52003.2023.10252933
• 发表时间: 2023
• 期刊: IEEE Power Energy Society General Meeting
• 作者: Lin, Yanbin;Das, Avijit;Ni, Zhen
• 通讯作者: Ni, Zhen

A new deep Q-learning method with dynamic epsilon adjustment and path planner assisted techniques for Turtlebot mobile robot

• DOI: 10.1117/12.2663695
• 发表时间: 2023-06
• 作者: W. Cheng;Zhengbin Ni;Xiangnan Zhong

Approximate dynamic programming with policy-based exploration for microgrid dispatch under uncertainties

• DOI: 10.1016/j.ijepes.2022.108359
• 发表时间: 2022-05-31
• 期刊: INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
• 影响因子: 5.2
• 作者: Das, Avijit;Wu, Di;Ni, Zhen
• 通讯作者: Ni, Zhen

A Neural-Reinforcement-Learning-based Guaranteed Cost Control for Perturbed Tracking Systems

基于神经强化学习的扰动跟踪系统保证成本控制

• DOI: 10.1109/tai.2023.3346334
• 发表时间: 2023
• 期刊: IEEE Transactions on Artificial Intelligence
• 作者: Zhong, Xiangnan;Ni, Zhen
• 通讯作者: Ni, Zhen

Multi-Virtual-Agent Reinforcement Learning for a Stochastic Predator-Prey Grid Environment

• DOI: 10.1109/ijcnn55064.2022.9891898
• 发表时间: 2022-07
• 期刊: 2022 International Joint Conference on Neural Networks (IJCNN)
• 作者: Yanbin Lin;Z. Ni;Xiangnan Zhong