Information for Prospective Students

The Reinforcement Learning and Artificial Intelligence (RLAI) laboratory is always looking for top students to join it in advancing the frontiers of research.

Undergraduate Students

The best way to prepare for research in artificial intelligence is by getting a basic grounding in mathematics and all the sciences, but particularly in biology, chemistry, physics, psychology, computing science, calculus, linear algebra, probability, artificial intelligence, machine learning, philosophy, and statistics.

There may be opportunities for undergraduate students to participate in research within the RLAI lab, but to do so effectively you will need to be familiar with the basics of artificial intelligence and reinforcement learning. The best ways to do that if you are at the University of Alberta is to take CMPUT 366, "Introduction to Intelligent Systems". If you can't do that, then you can still read and study the books "Reinforcement Learning: An Introduction" by Sutton and Barto and "The Quest for Artificial Intelligence" by Nils Nilsson, which are used in CMPUT 366. Another undergraduate course here that is highly recommended is CMPUT 466 "Introduction to Machine Learning".

Prospective MSc Students

The most common way to join the RLAI lab is as a graduate student in the MSc program in the department of computing science. Masters students in other departments, such as psychology, mathematics and statistics, or philosophy, are also welcome if they are prepared and motivated to pursue a principled approach to understanding the mind.

The computing science department has rigorous standards for admission. Requirements include high grades, good letters of recommendation, and, except for canadian students, good GRE and TOEFL exam scores. For grades, we look for a minimum of 3.0 on a 4 point scale, but we also pay special attention to the quality of school at which the grades were obtained. For the GRE, we look for a minimum of 155 quantitative and 4.0 analytic writing. On the internet TOEFL, the minimum score is 100. Many students have difficulty obtaining a 4.0 in analytic writing and have to be excluded for that reason. This is a shame but appropriate; you cannot succeed in studies and in science here without being able to write clearly in English. It is better to take an extra year and improve your English than to struggle with difficult ideas in a language that you do not know well.

After you are admitted to the University and Department, you can begin the process of finding a faculty member to serve as your supervisor. In exceptional cases this is worked out prior to your arrival. More typically you arrive and take some classes, which enables the professors to get to know you, and you to know them, and then it is easier to find the best match. There are currently 5 professors that are principal investigators of the RLAI lab: Rich Sutton, Csaba Szepesvari, Mike Bowling, Dale Schuurmans, and Patrick Pilarsky. Also affiliated with the RLAI lab are professor Martin Mueller and adjunct professors Andras Gyorgy and Joseph Modayil. And of course their are many other AI professors in the department.

If you would like to explore the possibility of being supervised by Rich Sutton, then you should take his graduate course CMPUT 609, Reinforcement Learning and Artificial Intelligence, which is typically taught each year in the Fall.

Prospective PhD Students

It is difficult to enter the computing science graduate program as a PhD student because it is our policy only to accept PhD students if their supervisor can be determined in advance of arrival. This is a major committment by the professor, and is often only possible when the student is already known to the professor, or if the student is recommended to the professor by someone they know well. More commonly, students are admitted as MSc students, then enter the PhD program after completed their MSc. It is also possible to enter as an MSc student and then be promoted to a PhD student without completing an MSc degree.