CM30359: Reinforcement learning
[Page last updated: 26 October 2022]
Academic Year:  2022/23 
Owning Department/School:  Department of Computer Science 
Credits:  6 [equivalent to 12 CATS credits] 
Notional Study Hours:  120 
Level:  Honours (FHEQ level 6) 
Period: 

Assessment Summary:  CW 100% 
Assessment Detail: 

Supplementary Assessment: 

Requisites: 
Before taking this module you must take CM10310 OR take CM20220 OR take CM20252
In taking this module you cannot take CM50270 
Learning Outcomes:  On completion of the unit, the students will be able to:
1. formulate reinforcement learning problems by defining a state space, an action space, and a reward function, appropriate for the context, 2. apply a range of solution methods to reinforcement learning problems, 3. appreciate the difficulties encountered in solving large, complex reinforcement learning problems in practice. 
Aims:  To explore reinforcement learning as an approach to artificial intelligence; to understand how reinforcement learning differs from other approaches to machine learning such as supervised and unsupervised learning; to learn how to formulate and solve reinforcement learning problems, and to appreciate the difficultes involved in solving large, complex reinforcement learning problems in practice. 
Skills:  Intellectual skills:
* Conceptual understanding of sequential decision making under uncertainty (T, F, A) Practical skills: * Programming reinforcement learning algorithms (T, F, A) * Use of software libraries for reinforcement learning (T, F, A) Transferable skills: * Technical report writing (F, A) 
Content:  Topics covered normally include: dynamic programming, Monte Carlo methods, temporaldifference algorithms (e.g., Qlearning), integration of planning and learning, value function approximation (e.g., with deep neural networks), policygradient methods, application areas, and an introduction to active areas of research (e.g., hierarchical reinforcement learning, intrinsicallymotivated reinforcement learning systems). 
Programme availability: 
CM30359 is Optional on the following programmes:Department of Computer Science

Notes:
