Optimal Control and Decision Theory

3.0 credits · Graduate

Covers the basic models and solution approaches for individual and team decision-making problems under uncertainty and provide a unified mathematical treatment of the subject, suitable for a broad engineering audience. The material will consider optimal decision-making of systems over a finite- and an infinite-time horizon. Topics include: (1) Stochastic optimization: finite- and infinite-horizon problems with complete or partial state information, separation principle, dual control; (2) Team Theory: mathematical framework of cooperating members in which all members have the same objective yet different information; (3) Reinforcement learning: approximate dynamic programming, forward references to the approximate dynamic programming formalism, learning policies.