CM50270: Reinforcement learning
[Page last updated: 23 October 2023]
| Academic Year: | 2023/24 |
| Owning Department/School: | Department of Computer Science |
| Credits: | 6 [equivalent to 12 CATS credits] |
| Notional Study Hours: | 120 |
| Level: | Masters UG & PG (FHEQ level 7) |
| Period: |
- Semester 2
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| Assessment Summary: | CW 100% |
| Assessment Detail: |
- Case Study (Written Report and Presentation) (CW 70%)
- Programming Assignments (CW 30%)
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| Supplementary Assessment: |
- Like-for-like reassessment (where allowed by programme regulations)
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| Requisites: |
In taking this module you cannot take CM30359
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| Learning Outcomes: |
At the end of this unit, students will be able to:
1. describe how reinforcement learning problems differ from supervised learning problems such as regression and classification,
2. formulate suitable real-world problems as reinforcement learning problems by defining a state space, an action space, and a reward function appropriate for the context,
3. critically evaluate a range of basic solution methods to reinforcement learning problems,
4. analyse the difficulties encountered in solving large, complex reinforcement learning problems in practice.
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| Aims: | This unit introduces the reinforcement learning problem and describes basic solution methods.
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| Skills: | Intellectual skills:
* Develop algorithmic thinking for sequential decision making under uncertainty (T, F, A)
Transferable skills:
* Enhance perspective of decision making (T, F)
* Oral presentation of ones work (F,A)
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| Content: | Topics covered normally include: dynamic programming, Monte Carlo methods, temporal-difference algorithms, integration of planning and learning, value function approximation, and policy gradient methods.
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| Course availability: |
CM50270 is Optional on the following courses:
Department of Computer Science
- RSCM-AFM51 : Integrated PhD Accountable, Responsible and Transparent Artificial Intelligence
- RSCM-APM51 : Integrated PhD Accountable, Responsible and Transparent Artificial Intelligence
- TSCM-AFM51 : MRes Accountable, Responsible and Transparent Artificial Intelligence
- TSCM-AFM52 : MSc Accountable, Responsible and Transparent Artificial Intelligence
- TSCM-AFM39 : MSc Computer Science
- TSCM-AFM45 : MSc Data Science
- TSCM-AWM45 : MSc Data Science
- TSCM-AFM48 : MSc Machine Learning and Autonomous Systems
- TSCM-AWM48 : MSc Machine Learning and Autonomous Systems
- USCM-AFM01 : MComp(Hons) Computer Science (Year 4)
- USCM-AAM02 : MComp(Hons) Computer Science with Study year abroad (Year 5)
- USCM-AKM02 : MComp(Hons) Computer Science with Year long work placement (Year 5)
- USCM-AFM14 : MComp(Hons) Computer Science and Mathematics (Year 4)
- USCM-AAM14 : MComp(Hons) Computer Science and Mathematics with Study year abroad (Year 5)
- USCM-AKM14 : MComp(Hons) Computer Science and Mathematics with Year long work placement (Year 5)
Department of Electronic & Electrical Engineering
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Notes: - This unit catalogue is applicable for the 2023/24 academic year only. Students continuing their studies into 2024/25 and beyond should not assume that this unit will be available in future years in the format displayed here for 2023/24.
- Courses and units are subject to change in accordance with normal University procedures.
- Availability of units will be subject to constraints such as staff availability, minimum and maximum group sizes, and timetabling factors as well as a student's ability to meet any pre-requisite rules.
- Find out more about these and other important University terms and conditions here.
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