Machine learning techniques are increasingly employed in a wide range of areas to model and analyze data as well
as to facilitate decision support and autonomous decision making by computer systems. Reinforcement learning is an important machine learning paradigm in particular in the context of decision support and decision making, but also in the context of modeling when only limited feedback is available. This course will introduce the Reinforcement Learning paradigm and its underlying formalisms, and will cover a wide range of basic and advanced Reinforcement Learning algorithms as well as aspects of model learning, hierarchy and abstraction, and reward modeling. Throughout, this course will study these techniques in the context of a wide range of application areas, including robotics, computer vision, security, control, scheduling, and data analysis.
Students completing this course will gain an understanding of the field and be able to apply modern, state-of-the-art Reinforcement Learning techniques to a wide range of problems and applications.
Many of the techniques covered in this course are based on probabilities and random processes
and a basic background in statistics is required for the course.
Prerequisites for the course are an advanced statistics and random processes course (CSE 5301 or similar),
or consent of instructor. In addition, experience with programming will
be useful for assignments and projects.
The course will mainly use the following textbook:
R. Sutton and A. Barto, Reinforcement Learning: An Introduction, Second Edition, MIT Press, 2018.
In addition the course will use readings from other books as well as
papers from technical conferences and journals. These materials will be made available through the
engineering library or the course site.
E-mail and WWW page:
There is a course web page at
http://www-cse.uta.edu/1#1huber/cse6369_rl as well as a Canvas page. All
changes and supplementary course materials will be
made available from Canvas and usually through the web site. In addition, necessary
changes or important announcements will also be
distributed through Canvas.
Tentative Office Hours:
Office hours for the course will be held by the
instructor in ERB 128 or in ERB 522,
TTh 5:00 - 6:00, and M 6:00 - 7:00. Times are subject to change and will be
posted. If you can not make it to any
of these office hours, please inform the
Course related emails should be sent to the instructor at firstname.lastname@example.org and should list the course number in the subject line.
There will be a Teaching Assistant for this course. Their details and Office Hours will be announced on Canvas.