CM50270: Reinforcement learning
Academic Year: | 2019/0 |
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) |
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Assessment Summary: | CW 100% |
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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
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