by Kardi Teknomo
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In this tutorial, you will discover step by step how an agent learns through training without teacher in unknown environment. Reinforcement learning is training paradigm for agents in which we have example of problems but we do not have the immediate exact answer. For playing a game, for instance, an agent will make series of decisions to move and only later will find out whether those decisions are right or wrong. Reinforcement learning paradigm is similar to real life of how we learn.
In this tutorial, you will find out part of reinforcement learning algorithm called Qlearning. Reinforcement learning algorithm has been widely used for many applications such as robotics, multi agent system, game, motion planning, navigation, and etc.
Instead of learning the theory of reinforcement that you can read it from many books and other web sites (see Resources for more references), this tutorial will introduce the concept through simple but comprehensive numerical examples. If you purchase the ebook of this tutorial, you will also receive the companion worksheet and the matlab files.
Read this tutorial comfortably offline on any device. Click here to purchase the complete Ebook of this tutorial
Let us start the tutorial (clicks the topic below).
Modeling the Environment
Agent, State and Action Introduction
Q Learning
Q Learning Algorithm
Numerical Example
Another Q learning Example: Tower of Hanoi
QLearning Solution for Tower of Hanoi
Q Learning using Matlab
Q Learning using MS Excel
Practice make perfect
Resources
Click here to purchase the complete Ebook of this tutorial
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This tutorial is copyrighted.
Preferable reference for this tutorial is
Teknomo, Kardi. 2005. QLearning by Examples. http://people.revoledu.com/kardi/tutorial/ReinforcementLearning/index.html
