Course:  Reinforcement Learning


Time in Year: 
Course runs in Semester 1.

Number of Posts: 

This course is a MSc level introduction to reinforcement learning. The first 60% of this course will cover the standard material in Sutton and Barto's textbook. The latter 40% will focus on more state of the art topics ranging from policy gradient algorithms to multiagent RL.

The course includes two homework assignments and 3-4 tutorial sessions.

The tutorials will consist of specific exercises set in advance of the tutorial, and all students are expected to prepare these prior to the tutorial session. However, it is likely that the students will require clarifications on the concepts. So, the tutor's job is to lead the group through the exercises, answer questions etc.

The tutor will also be expected to mark the two homework assignments for this course (to be verified by the instructor before official assignment of the marks). The homework assignments will include a programming component.

The tutor will need to be familiar with the topics mentioned above. Having done well in machine learning courses (preferably, including one on RL) would be suitable preparation.

This post is paid at grade UE06, currently upwards of £12.50/hr.

Total Hours: 
5 contact hours + 10 preparation hours

Subramanian Ramamoorthy

Apply by email to and copy your email to the instructor. 

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