CS 671: Learning and Sequential Decision Making

Rutgers University
Fall 2005
Michael L. Littman

Time: Tuesday, Thursday 1:40-3:00
Place: Rutgers, Hill 482
Semester: Fall 2005

Michael's office hours: Hill 409, by appointment (mlittman@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 a benchmark set of problems. 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.

News (most recent first)

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/rl05/.