Through a combination of classic papers and more recent work, the course explores automated decision making from a computational perspective. It examines efficient algorithms, where they exist, for single-agent and multi-agent planning as well as approaches to learning near-optimal decisions from experience.
Topics 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. Students will replicate a result in a published paper in the area.
This course counts towards the following specialization(s):
Spring 2019 syllabus (PDF)
Note: Sample syllabi are provided for informational purposes only. For the most up-to-date information, consult the official course documentation.
You can view the lecture videos for this course here.
Before Taking This Class...
Suggested Background Knowledge
Successful completion of CS 7641: Machine Learning is strongly recommended. Students should be familiar with object-oriented programming, preferably Python.
Technical Requirements and Software
- Browser and connection speed: An up-to-date version of Chrome or Firefox is strongly recommended. We also support Internet Explorer 9 and the desktop versions of Internet Explorer 10 and above (not the metro versions). 2+ Mbps is recommended; the minimum requirement is 0.768 Mbps download speed.
- Operating system:
- Ubuntu Linux 16.04 or higher (highly recommended for assignments
- PC: Windows XP or higher with latest updates installed, or Mac: OS X 10.6 or higher with latest updates installed will be required for the final exam
All Georgia Tech students are expected to uphold the Georgia Tech Academic Honor Code. This course may impose additional academic integrity stipulations; consult the official course documentation for more information.