6.231 | Fall 2015 | Graduate
Dynamic Programming and Stochastic Control
Course Description
The course covers the basic models and solution techniques for problems of sequential decision making under uncertainty (stochastic control). We will consider optimal control of a dynamical system over both a finite and an infinite number of stages. This includes systems with finite or infinite state spaces, as well as …
The course covers the basic models and solution techniques for problems of sequential decision making under uncertainty (stochastic control). We will consider optimal control of a dynamical system over both a finite and an infinite number of stages. This includes systems with finite or infinite state spaces, as well as perfectly or imperfectly observed systems. We will also discuss approximation methods for problems involving large state spaces. Applications of dynamic programming in a variety of fields will be covered in recitations.
Learning Resource Types
grading Exams with Solutions
notes Lecture Notes
theaters Other Video
assignment Problem Sets
Diagram in which nodes can be inserted into or removed from a list of active nodes.
Label correcting methods for shortest paths. See Lecture 3 for more information. (Figure by MIT OpenCourseWare, adapted from course notes by Prof. Dimitri Bertsekas.)