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CM50270: Reinforcement learning

Academic Year: 2018/9
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
Assessment Summary: CW 100%
Assessment Detail:
  • Written Project Report + Oral Presentation (CW 40%)
  • Programming Assignments (CW 60%)
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Requisites:
Description: 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

Notes:

  • This unit catalogue is applicable for the 2018/19 academic year only. Students continuing their studies into 2019/20 and beyond should not assume that this unit will be available in future years in the format displayed here for 2018/19.
  • Programmes 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.
  • Undergraduates: .
  • Postgraduates: .