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R Deep Learning Cookbook

You're reading from   R Deep Learning Cookbook Solve complex neural net problems with TensorFlow, H2O and MXNet

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781787121089
Length 288 pages
Edition 1st Edition
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Authors (2):
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Achyutuni Sri Krishna Rao Achyutuni Sri Krishna Rao
Author Profile Icon Achyutuni Sri Krishna Rao
Achyutuni Sri Krishna Rao
PKS Prakash PKS Prakash
Author Profile Icon PKS Prakash
PKS Prakash
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Toc

Table of Contents (11) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Deep Learning with R 3. Convolution Neural Network 4. Data Representation Using Autoencoders 5. Generative Models in Deep Learning 6. Recurrent Neural Networks 7. Reinforcement Learning 8. Application of Deep Learning in Text Mining 9. Application of Deep Learning to Signal processing 10. Transfer Learning

Setting up a Markov Decision Process


The Markov Decision Process (MDP) forms the basis of setting up RL, where the outcome of a decision is semi-controlled; that is, it is partly random and partly controlled (by the decision-maker). An MDP is defined using a set of possible states (S), a set of possible actions (A), a real-values reward function (R), and a set of transition probabilities from one state to another state for a given action (T). In addition, the effects of an action performed on one state depends only on that state and not on its previous states.

Getting ready

In this section, let us define an agent travelling across a 4 x 4 grid, as shown in following figure:

A sample 4 x 4 grid of 16 states

This grid has 16 states (S1, S2....S16). In each state, the agent can perform four actions (up, right, down, left). However, the agent will be restricted to some actions based on the following constraints:

  • The states across the edges shall be restricted to actions which point only toward states...
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