Reinforcement learning is a highly active area of research, blending ideas and techniques from control, optimization, machine learning, and computer science. Given this diversity of viewpoints and frameworks, it is imperative to understand their strengths and their limitations. The aim of this iDS2 virtual mini-workshop is a constructive dialogue and exchange of ideas between researchers in these fields. It will feature two tutorial-style talks emphasizing the asymptotic and the non-asymptotic perspectives, followed by a moderated discussion featuring the speakers.
All events in this mini-workshop will be take place on
Fridays, April 16, April 23, April 30
from 12:30 pm to 3:00 pm CST, with a break from 1:30 pm to 2:00 pm
REGISTER for the mini-workshop
April 16, 2021
Stochastic Control Problems, and How You Can Solve Yours
- S. Chen, A. M. Devraj, F. Lu, A. Busic, and S. Meyn. Zap Q-Learning with nonlinear function approximation. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, and arXiv e-prints 1910.05405, volume 33, pages 16879–16890. Curran Associates, Inc., 2020.
- A. M. Devraj, A. Busic and S. Meyn. Fundamental design principles for reinforcement learning algorithms. In K. G. Vamvoudakis, Y. Wan, F. L. Lewis, and D. Cansever, editors, Handbook on Reinforcement Learning and Control. Springer, 2021.
- S. Meyn. Control Systems and Reinforcement Learning. Cambridge University Press, 2021 (current draft available here)
April 23, 2021
Non-Stochastic Control Theory
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.
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.
April 30, 2021