Reinforcement Learning

Reinforcement Learning

Darryl Charles (University of Ulster, Ireland), Colin Fyfe (University of Paisley, UK), Daniel Livingstone (University of Paisley, UK) and Stephen McGlinchey (University of Paisley, UK)
Copyright: © 2008 |Pages: 25
DOI: 10.4018/978-1-59140-646-4.ch012
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Abstract

Just as there are many different types of supervised and unsupervised learning, so there are many different types of reinforcement learning. Reinforcement learning is appropriate for an AI or agent which is actively exploring its environment and also actively exploring what actions are best to take in different situations. Reinforcement learning is so-called because, when an AI performs a beneficial action, it receives some reward which reinforces its tendency to perform that beneficial action again. An excellent overview of reinforcement learning (on which this brief chapter is based) is by Sutton and Barto (1998).

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Table of Contents
Foreword
Alex J. Champandard
Acknowledgment
Chapter 1
Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen McGlinchey
This chapter provides a brief outline of the history of video game AI – and hence by extension an extremely brief outline of some of the key points... Sample PDF
Contemporary Video Game AI
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Chapter 2
Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen McGlinchey
The design of the first computers were influenced by the power of the human brain and attempts to create artificial intelligence, yet modern day... Sample PDF
An Introduction to Artificial Neural Networks
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Chapter 3
Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen McGlinchey
In this chapter we will look at supervised learning in more detail, beginning with one of the simplest (and earliest) supervised neural learning... Sample PDF
Supervised Learning with Artificial Neural Networks
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Chapter 4
Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen McGlinchey
In this short chapter we present a case study of the use of ANN in a video game type situation. The example is one of duelling robots, a problem... Sample PDF
Case Study: Supervised Neural Networks in Digital Games
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Chapter 5
Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen McGlinchey
With the artificial neural networks which we have met so far, we must have a training set on which we already have the answers to the questions... Sample PDF
Unsupervised Learning in Artificial Neural Networks
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Chapter 6
Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen McGlinchey
We noted in the previous chapters that, while the multilayer perceptron is capable of approximating any continuous function, it can suffer from... Sample PDF
Fast Learning in Neural Networks
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Chapter 7
Genetic Algorithms  (pages 105-120)
Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen McGlinchey
The methods in this chapter were developed in response to the need for general purpose methods for solving complex optimisation problems. A... Sample PDF
Genetic Algorithms
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Chapter 8
Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen McGlinchey
The last two chapters introduced the standard GA, presented an example case study and explored some of the potential pitfalls in using evolutionary... Sample PDF
Beyond the GA: Extensions and Alternatives
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Chapter 9
Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen McGlinchey
Multi-Objective Problems, MOP, are a class of problems for which different, competing, objectives are to be satisfied and for which there is... Sample PDF
Evolving Solutions for Multiobjective Problems and Hierarchical AI
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Chapter 10
Artificial Immune Systems  (pages 150-179)
Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen McGlinchey
We now consider the problem of introducing more intelligence into the artificial intelligence’s responses in real-time strategy games (RTS). We... Sample PDF
Artificial Immune Systems
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Chapter 11
Ant Colony Optimisation  (pages 180-201)
Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen McGlinchey
Ants are truly amazing creatures. Most species of ant are virtually blind; some of which have no vision at all, yet despite this, they are able to... Sample PDF
Ant Colony Optimisation
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Chapter 12
Reinforcement Learning  (pages 202-226)
Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen McGlinchey
Just as there are many different types of supervised and unsupervised learning, so there are many different types of reinforcement learning.... Sample PDF
Reinforcement Learning
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Chapter 13
Adaptivity within Games  (pages 227-238)
Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen McGlinchey
This book centres on biologically inspired machine learning algorithms for use in computer and video game technology. One of the important reasons... Sample PDF
Adaptivity within Games
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Chapter 14
Darryl Charles, Colin Fyfe, Daniel Livingstone, Stephen McGlinchey
It is very evident that current progress in developing realistic and believable game AI lags behind that in developing realistic graphical and... Sample PDF
Turing's Test and Believable AI
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About the Contributors