CS 672: Learning and Sequential Decision Making

Rutgers University
Spring 2004
Michael L. Littman

Time: Monday, Wednesday 4:30-5:50
Place: Rutgers, Hill Center 482/484
Semester: Spring 2004

Michael's office hours: Hill 409, Wednesday 2pm-3pm and by appointment (mlittman@cs.rutgers.edu).


Description: Through a combination of classic papers and more recent work, the course will explore automated decision making from a computer-science perspective. It will examine 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 agent to compete in a video game environment. 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. This is the first time the course is being offered.

News (most recent first)

Topics and Papers

Throughout the semester we will be reading sections of the RL survey by Kaelbling, Littman, Moore (1996).

RL Links

The URL for this page is http://www.cs.rutgers.edu/~mlittman/courses/rl04/.