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

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. 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.

Abstract retrieved from Professor's Personal Page.