EE3xx: Dynamic Programming and Optimal Control
Dynamic programming is a framework for deriving optimal decision strategies in evolving and uncertain environments. Topics include the principle of optimality in deterministic and stochastic settings, value and policy iteration, connections to Pontryagin maximum principle, imperfect state measurement problems, and simulation-based methods such as online reinforcement learning.
EE271A/ME221A: Control Theory A
This course presents fundamental topics for the analysis of linear dynamical systems, i.e., systems that evolve in time that are admit an underlying linear structure. The material in this course serves as the foundation for continued study in more advanced courses in control design and system theory.
EE392D: Advanced Topics: Game Theory and Multiagent Systems
This course is an introduction to game theory, which is the study of interacting decision makers. The course covers the basic framework for strategic games and its various manifestations. Topics include matrix games, extensive form games, mixed strategies, repeated games, Bayesian games, and cooperative games. The course continues with an application of game theory as a design tool for multiagent systems, i.e., systems that comprised of a collection of programmable decision-making components. Examples are drawn from engineered, economic, and social models.