Undergraduate Course: Robot and Reinforcement Learning (UG) (INFR11290)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Year 4 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course follows the delivery and assessment of Robot and Reinforcement Learning (INFR11285) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11285 instead. |
| Course description |
This course follows the delivery and assessment of Robot and Reinforcement Learning (INFR11285) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11285 instead.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | Students MUST NOT also be taking
Robot and Reinforcement Learning (INFR11285)
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Other requirements | This course follows the delivery and assessment of Robot and Reinforcement Learning (INFR11285) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11285 instead.
Students are expected to be proficient in mathematical concepts, particularly:
Probability theory and statistics.
Linear algebra and geometry (e.g., vectors, matrices, rotations, trigonometry).
Calculus (e.g., integration, differentiation)
Students are also expected to be proficient in a programming language, ideally python, and familiar with working in Linux OS systems. |
Information for Visiting Students
| Pre-requisites | This course follows the delivery and assessment of Robot and Reinforcement Learning (INFR11285) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11285 instead.
Students are expected to be proficient in mathematical concepts, particularly:
Probability theory and statistics.
Linear algebra and geometry (e.g., vectors, matrices, rotations, trigonometry).
Calculus (e.g., integration, differentiation)
Students are also expected to be proficient in a programming language, ideally python, and familiar with working in Linux OS systems. |
Course Delivery Information
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| Academic year 2025/26, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 26,
Seminar/Tutorial Hours 2,
Summative Assessment Hours 2,
Revision Session Hours 2,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
164 )
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| Assessment (Further Info) |
Written Exam
70 %,
Coursework
30 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Written Exam __70%«br /»
Coursework __30% |
| Feedback |
Example problem sets and revision lectures will be provided to help with exam preparation. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- explain key concepts of sequential decision making, reinforcement learning, and robot planning and control.
- analyze and evaluate decision-making problems in robot control, and identify those to which learning methods can be applied.
- implement and analyse a subset of established learning algorithms for dynamics learning and stochastic control.
- explain limitations of current learning and optimisation algorithms in the context of robot control problems.
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Reading List
(i) Reinforcement Learning: An Introduction (second edition). R. Sutton and A. Barto. MIT Press, 2018.
(ii) Algorithms for Reinforcement Learning. C. Szepesvari. Morgan and Claypool Publishers, 2010. |
Additional Information
| Graduate Attributes and Skills |
Problem solving, critical/analytical thinking, handling ambiguity, knowledge integration, planning and organizing, independent learning, creativity, written communication. |
| Keywords | FFL |
Contacts
| Course organiser | Dr Michael Mistry
Tel: (0131 6)50 2937
Email: mmistry@exseed.ed.ac.uk |
Course secretary | Miss Toni Noble
Tel: (0131 6)50 2692
Email: Toni.noble@ed.ac.uk |
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