Machine Learning, Dynamical Systems and Control



We will leverage the remarkable successes of ML towards the control of modern complex dynamical systems. Reinforcement learning (RL) is a class of ML that addresses the problem of learning to control physical systems by explicitly considering their inherent dynamical structure and feedback loop. To date, the successes of RL have been limited to very structured or simulated environments, and its successes in real-world systems are few. RL has significant challenges involving: (i) Scalability: How to develop scalable RL methods for large-size network multiagent (MARL) dynamical systems? (ii) Robustness: How to maintain the performance (efficiency and safety) of the learned policies even when there is a model class mismatch? (iii) Safety: How to guarantee RL maintains stability and stays in the safe constraint while still learning efficiently? We will develop critically enabling mathematical and computational architectures to overcome these challenges which are present in a diverse number of applications involved complex dynamical systems.




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Na Li, Thrust Lead: AI Control
Gordon McKay Professor

Website: [ VIEW ]

Department of Electrical Engineering and Applied Mathematics


Research: Control theory, networked systems, optimization, cyber-physical systems