The University of Sheffield
Department of Computer Science

COM3240 Reinforcement Learning

Summary

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, Actor Critic Architectures and 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.
Session Spring 2023/24
Credits 10
Assessment
  • Assignment
Lecturer(s) Prof. Eleni Vasilaki
Resources
Aims This module aims to teach students the theory and implementation of bio-inspired machine learning algorithms.
Learning Outcomes  By the end of the module the student will be able to:
  • demonstrate understanding of the working principles and mathematical theory of reinforcement learning algorithms.
  • demonstrate the ability to implement these methods from the first principles.
  • demonstrate the ability to apply these methods to sample problems.
Content Topics include:
  • Supervised learning: the backpropagation algorithm (as prerequisite for Deep reinforcement Learning)..
  • Reinforcement Learning: (Temporal Difference Learning: Q learning, SARSA, Deep Reinforcement Learning, Actor Critic Architectures, Advanced topics.  
Restrictions
 COMU101, COMU103, COMU06, COMU05, COMU117, COMU109, COMU118. Students from departments other than Computer Science will need to demonstrate an excellent understanding of programming (Python or Matlab) and mathematics. A level math is compulsory.
Teaching Method The course will consist of a series of a one hour lecture and a two hours practical lab session each week. These will be backed up by exercises and homework. Group discussion of issues relevant to the course will be encouraged. Self-study of the course is also required, supported by a variety of resources (e.g. journal articles, books).
Feedback Students will receive feedback on assignments in writing via Blackboard and via an in-class feedback session. Feedback will also be delivered during labs and office hours (upon request).