COM3240 Reinforcement Learning

Aims/Description: This module aims to teach students the theory and implementation of reinforcement learning. Topics include: Supervised learning: the backpropagation algorithm (as prerequisite for Deep reinforcement learning). Reinforcement Learning: Temporal Difference Learning (Q learning, SARSA), Deep Reinforcement Learning, Advanced Topics. As well as the material taught in class, students are expected to self-study relevant books and research articles and produce reports in research article styles.

Restrictions on availability: COMU101, COMU103, COMU06, COMU05, COMU117, COMU109, COMU118. Students from schools other than Computer Science will need to demonstrate an excellent understanding of programming (Python or Matlab) and mathematics. A level math is compulsory.

Staff Contact: VASILAKI ELENI

Teaching Methods: Lectures, Laboratory work, Independent Study

Assessment: Formal Exam

Notes: This unit forms part of an accredited degree programme

Information on the department responsible for this unit (Computer Science):

Departmental Home Page

Teaching timetable

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Teaching methods and assessment displayed on this page are indicative for 2025-26.