Reinforcement Learning (P00881)

? Credit Points : 10 ? SCQF Level : 11 ? Acronym : INF-P-RL

This module covers a range of adaptive learning systems, in particular reinforcement learning and unsupervised methods, particularly as used in RL systems. By the end of the module the student should have a grasp of modern learning techniques and the issues involved in dealing with real-world data. The main techniques covered in the course are basic reinforcement learning, dynamic programming, Monte Carlo methods, Q-learning, function approximation, unsupervised and constructive methods, radial basis and other local functions, classifier systems as compared to RL systems.

Entry Requirements

? Pre-requisites : PGs only or with permission of Director of Teaching.

Subject Areas

Home subject area

Artificial Intelligence, (School of Informatics, Schedule O)

Delivery Information

? Normal year taken : Postgraduate

? Delivery Period : Semester 2 (Blocks 3-4)

? Contact Teaching Time : 2 hour(s) per week for 10 weeks

First Class Information

Date	Start	End	Room	Area	Additional Information
09/01/2006	12:00	13:00	Lecture Room 3218, JCMB	KB

All of the following classes

Type	Day	Start	End	Area
Lecture	Monday	12:10	13:00	KB
Lecture	Thursday	12:10	13:00	KB

Summary of Intended Learning Outcomes

-Knowledge of basic and advanced reinforcement learning techniques.
-Insight into the problems involved in applying these techniques to deal with real world data, and how to overcome those problems.
-Appreciation and identification of suitable learning tasks to which these learning techniques can be applied
-Ability to evaluate how effective a particular learning procedure has been -- internal indicators of learning success vs. actual behaviour of the learner.
-Use and writing of Matlab programs, ability to set up and run computational experiments to produce statistically sound results
-Formulation of problems, evaluation of results from the student's own experiments and those presented in some cases in the research literature.