CSCI2951-F: Learning and Sequential Decision Making

Brown University
Fall 2012
Michael L. Littman

Time: MW 3-4:20
Place: Brown CIT 506
Semester: Fall 2012

Michael's office hours: CIT 301, by appointment (mlittman@cs.brown.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. Depending upon enrollment, each student may be expected to present a published research paper and will participate in a group project to create a reinforcement-learning system for a video game. 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.

Calendar

Suggested Project Papers

Upcoming Papers/Topics

Other Papers

Sutton (1990)
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).

Other Topics I'd Love To Talk About

UCT and Go. Recent Alberta work on function approximation. Bayesian RL. Natural policy gradient. RL in Neuroscience. Unlearning in SARSA(0) in Tetris. Ramon et al..

RL Links

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