2.997 Decision Making in Large Scale Systems

Spring 2004

Diagram of the game Tetris.
Tetris game strategies can be analyzed using the value function approximation techniques described in this course -- see lecture session 10.  (Illustration courtesy of OCW.)

Course Highlights

This course features extensive lecture notes and readings, plus selected examples of student projects.

Course Description

This course is an introduction to the theory and application of large-scale dynamic programming. Topics include Markov decision processes, dynamic programming algorithms, simulation-based algorithms, theory and algorithms for value function approximation, and policy search methods. The course examines games and applications in areas such as dynamic resource allocation, finance and queueing networks.

Technical Requirements

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Staff

Instructor:
Prof. Daniela Pucci De Farias

Course Meeting Times

Lectures:
Two sessions / week
1.5 hours / session

Level

Graduate