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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2025/2026

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DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Reinforcement Learning (UG) (INFR11236)

This course will be closed from 31 July 2025

Course Outline
School	School of Informatics	College	College of Science and Engineering
Credit level (Normal year taken)	SCQF Level 11 (Postgraduate)	Availability	Available to all students
SCQF Credits	10	ECTS Credits	5
Summary	Following the closure of this course, a suggested replacement for students to consider is: Robot and Reinforcement Learning (UG) INFR11290. This course follows the delivery and assessment of Reinforcement Learning (INFR11010) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11010 instead.
Course description	This course follows the delivery and assessment of Reinforcement Learning (INFR11010) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11010 instead.

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 (INFR11010)	Other requirements	This course follows the delivery and assessment of Reinforcement Learning (INFR11010) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11010 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-requisites	As above.
High Demand Course?	Yes

Course Delivery Information
Not being delivered

Learning Outcomes
On completion of this course, the student will be able to: gain knowledge of basic and advanced reinforcement learning techniques identify suitable learning tasks to which these learning techniques can be applied appreciate the current limitations of reinforcement learning techniques 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
Course URL	https://opencourse.inf.ed.ac.uk/rl
Graduate Attributes and Skills	Not entered
Keywords	artificial intelligence,machine learning,reinforcement learning

Contacts
Course organiser	Dr Michael Herrmann Tel: (0131 6)51 7177 Email: Michael.Herrmann@ed.ac.uk	Course secretary	Ms Lindsay Seal Tel: (0131 6)50 5194 Email: lindsay.seal@ed.ac.uk

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