Stochastic Systems: Estimation and Control

Catalog description

The problem of sequential decision making in the face of uncertainty is ubiquitous. Examples include: dynamic portfolio trading, operation of power grids with variable renewable generation, air traffic control, livestock and fishery management, supply chain optimization, internet ad display, data center scheduling, and many more. In this course, we will explore the problem of optimal sequential decision making under uncertainty over multiple stages - stochastic optimal control. We will discuss different approaches to modeling, estimation, and control of discrete time stochastic dynamical systems (with both finite and infinite state spaces) over finite horizons. Solution techniques based on dynamic programming will play a central role in our analysis. Topics include: Fully and Partially Observed Markov Decision Processes, Linear Quadratic Gaussian control, Bayesian Filtering, and Approximate Dynamic Programming. Applications to various domains will be discussed throughout the semester.