CS 598: Learning and Sequential Decision Making

Rutgers University
Spring 2009
Michael L. Littman

Time: Thursday 1:40-4:40
Place: Rutgers, Hill 120
Semester: Spring 2009

Michael's office hours: Hill 409, Thu 1:00 and by appointment (mlittman@cs.rutgers.edu).
TA: Shu Chen's office hours: Hill 416, Thu 12:00pm-1:00pm (shuchen@cs.rutgers.edu)

Description: Through a combination of classic papers and more recent work, the course explores automated decision making from a computer-science perspective. It examines efficient algorithms, where they exist, for single agent and multiagent planning as well as approaches to learning near-optimal decisions from experience. Topics will include Markov decision processes, stochastic and repeated games, partially observable Markov decision processes, and reinforcement learning. Of particular interest will be issues of generalization, exploration, and representation. Each student will be expected to present a published research paper and will participate in a group project to create a reinforcement-learning system for this year's international reinforcement-learning competition. Participants should have taken a graduate-level computer science course and should have some exposure to reinforcement learning from a previous computer-science class or seminar; check with instructor if not sure.



Sutton (1990)
Kocsis and Szepesvári (2006), Silver, Sutton, and Mueller (2008). Optional: Chaslot, Winands, Herik, Uiterwijk, and Bouzy (2008)

Topics and Papers

The RL survey referred to below is Kaelbling, Littman, Moore (1996).

RL Links

The URL for this page is http://www.cs.rutgers.edu/~mlittman/courses/seq09/.