Postgraduate Course: Reinforcement Learning (INFR11010)
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 | Reinforcement 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
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Information for Visiting Students
Pre-requisites | As above. |
High Demand Course? |
Yes |
Course Delivery Information
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Academic year 2024/25, Available to all students (SV1)
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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 )
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Assessment (Further Info) |
Written Exam
50 %,
Coursework
50 %,
Practical Exam
0 %
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Additional Information (Assessment) |
Exam 50%
Coursework 50% |
Feedback |
Not entered |
Exam Information |
Exam Diet |
Paper Name |
Hours & Minutes |
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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:
- 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
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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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