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 : Learning from Data (Level 10/11) For Informatics PG students only, or by special permission of the School. Students should be familiar with the mathematical concepts therein, particularly vectors and matrices, partial differentiation, and some probability. Knowledge of the backpropagation algorithm is required.

? Co-requisites : Genetic Algorithms & Genetic Programming and Machine Learning for Sensorimotor Control may be appropriate complimentary courses, although not compulsory.

Subject Areas

Home subject area

Bioinformatics, (School of Informatics, Schedule O)

Other subject areas

Intelligent Robotics, (School of Informatics, Schedule O)

Vision, Perception and Action, (School of Informatics, Schedule O)

Learning from Data, (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
07/01/2008	16:10	17:00	Room G.04, William Robertson Building	Central

All of the following classes

Type	Day	Start	End	Area
Lecture	Monday	16:10	17:00	Central
Lecture	Thursday	16:10	17:00	Central

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.