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DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Reinforcement Learning (INFR11010)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryReinforcement learning (RL) refers to a collection of machine learning techniques which solve sequential decision making problems using a process of trial-and-error. It is a core area of research in artificial intelligence and machine learning, and today provides one of the most powerful approaches to solving decision problems. This course covers foundational models and algorithms used in RL, as well as advanced topics such as scalable function approximation using neural network representations and concurrent interactive learning of multiple RL agents.
Course description Main topics to be covered include the following:

* Reinforcement learning framework
* Bandit problems and action selection
* Dynamic programming
* Monte Carlo methods
* Temporal difference learning
* Planning in RL
* Function approximation for generalisation
* Actor-critic and gradient-based optimisation
* Multi-agent reinforcement learning
* Training agents and evaluating performance

Relevant QAA Computing Curriculum Sections: Artificial Intelligence, Data Structures and Algorithms, Intelligent Information Systems Technologies, Simulation and Modelling
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites It is RECOMMENDED that students also take ( Machine Learning Practical (INFR11132) OR Machine Learning Practical (UG) (INFR11223)) OR ( Machine Learning (INFR10086) OR Applied Machine Learning (INFR11211)) OR ( Probabilistic Modelling and Reasoning (INFR11134) OR Probabilistic Modelling and Reasoning (UG) (INFR11235))
Prohibited Combinations Students MUST NOT also be taking Reinforcement Learning (UG) (INFR11236)
Other requirements MSc students must register for this course, while Undergraduate students must register for INFR11236 instead.

This course is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your DPT, please seek special permission from the course organiser.

Mathematical background: at the level of undergraduate informatics, particularly linear algebra, multivariate calculus, probability theory, and statistics

Good understanding of Python programming, familiarity with the scientific package NumPy. Familiarity with PyTorch is helpful.
Information for Visiting Students
Pre-requisitesAs above.
High Demand Course? Yes
Course Delivery Information
Academic year 2024/25, Available to all students (SV1) Quota:  None
Course Start Semester 2
Course Start Date 13/01/2025
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 20, Seminar/Tutorial Hours 8, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 68 )
Assessment (Further Info) Written Exam 50 %, Coursework 50 %, Practical Exam 0 %
Additional Information (Assessment) Exam 50%
Coursework 50%
Feedback Not entered
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)Reinforcement Learning PG (INFR11010) UG (INFR11236)2:120
Learning Outcomes
On completion of this course, the student will be able to:
  1. gain knowledge of basic and advanced reinforcement learning techniques
  2. identify suitable learning tasks to which these learning techniques can be applied
  3. appreciate the current limitations of reinforcement learning techniques
  4. gain an ability to formulate decision problems, set up and run computational experiments, evaluation of results from experiments
Reading List
Reinforcement Learning: An Introduction (second edition). R. Sutton and A. Barto. MIT Press, 2018
Algorithms for Reinforcement Learning. C. Szepesvari. Morgan and Claypool Publishers, 2010
Reinforcement Learning: State-of-the-Art. M. Wiering and M. van Otterlo. Springer, 2012
Additional Information
Graduate Attributes and Skills Not entered
KeywordsArtificial Intelligence,Machine Learning,Reinforcement Learning
Contacts
Course organiserDr Stefano Albrecht
Tel: (0131 6)51 3218
Email: s.albrecht@ed.ac.uk
Course secretaryMs Lindsay Seal
Tel: (0131 6)50 2701
Email: lindsay.seal@ed.ac.uk
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