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CM50270: Reinforcement learning

[Page last updated: 18 October 2021]

Academic Year: 2021/2
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)
Semester 2
Assessment Summary: CW 100%
Assessment Detail:
  • Case Study (Written Report and Presentation) (CW 70%)
  • Programming Assignments (CW 30%)
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Aims: This unit introduces the reinforcement learning problem and describes basic solution methods.

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.

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)

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.

Programme availability:

CM50270 is Optional on the following programmes:

Department of Computer Science Department of Electronic & Electrical Engineering


  • This unit catalogue is applicable for the 2021/22 academic year only. Students continuing their studies into 2022/23 and beyond should not assume that this unit will be available in future years in the format displayed here for 2021/22.
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