Abstract: In this talk we will discuss an emerging paradigm in online and adaptive control. We will start by discussing linear dynamical systems that are a continuous subclass of reinforcement learning models widely used in robotics, finance, engineering, and meteorology. Classical control, since the work of Kalman, has focused on dynamics with Gaussian i.i.d. noise, quadratic loss functions and, in terms of provably efficient algorithms, known systems and observed state. We’ll discuss how to apply new machine learning methods which relax all of the above: provably efficient control with adversarial noise, general loss functions, unknown systems and partial observation. We will briefly survey recent work which applies this paradigm to black-box control, time-varying systems and planning in iterative learning control.
No background is required for this talk, but some materials can be found here and here
Based on a series of works with Naman Agarwal, Nataly Brukhim, Karan Singh, Sham Kakade, Max Simchowitz, Cyril Zhang, Paula Gradu, Brian Bullins, Xinyi Chen and Anirudha Majumdar.