Postgraduate Course: Reinforcement Learning (INFR11010)
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 INFR11285.
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
| 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
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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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