This course gives an introduction to decision theory and reinforcement learning, focusing on theory and algorithms. reinforcement learning problems. Each module lasts for about a week, and includes a short lecture of basic concepts, an overview of proofs and some hands-on work. Assessment is through assignments and a mini project.
An advanced version of this course is FDAT115, Advanced Topics in Reinforcement Learning and Decision Making
This is only a suggested schedule. Points in italics are optionally covered.
The course takes place in LP3/4 and is worth 7.5 credits.
Lecturer: Debabrota Basu (Some lectures by Christos Dimitrakakis)
Prerequisites
While the course will go over the basic ideas, the focus will be on fundamentals, proofs and algorithms. We will also run a section on statistical inference for those that have not taken any advanced statistics courses before.
Start date: 12 February. Go here to join the QA.
Wednesdays 10:00-12:00
or Thursdays, 14:00-16:00.
Detailed Schedule
The course mainly follows the structure of our draft book, "Decision Making Under Uncertainty and Reinforcement Learning".
Other useful books:
Other material will be referred to in the course assignments.
10% participation, 50% assignments, 40% project
Assignments. Completion of 4 assignments is required for the course, focusing on the fundamentals.
Project. A larger mini-project combining elements of the assignments for optimal exploration, will take place at the end of the course. The projects are done in groups of 2-3 students.