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Keras Reinforcement Learning Projects
Keras Reinforcement Learning Projects

Keras Reinforcement Learning Projects: 9 projects exploring popular reinforcement learning techniques to build self-learning agents

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Profile Icon Giuseppe Ciaburro
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€18.99 per month
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.3 (3 Ratings)
Paperback Sep 2018 288 pages 1st Edition
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€22.99 €32.99
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Arrow left icon
Profile Icon Giuseppe Ciaburro
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€18.99 per month
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.3 (3 Ratings)
Paperback Sep 2018 288 pages 1st Edition
eBook
€22.99 €32.99
Paperback
€41.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€22.99 €32.99
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€41.99
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Renews at €18.99p/m

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Keras Reinforcement Learning Projects

Simulating Random Walks

Stochastic processes involve systems that evolve over time (but also more generally in space) according to probabilistic laws. Such systems or models describe the complex phenomena of the real world that have the possibility of being random. These phenomena are more frequent than we can believe. We encounter these phenomena when the quantities we are interested in are not predictable with absolute certainty. However, when such phenomena show a variability of possible outcomes that can be somehow explained or described, then we can introduce a probabilistic model of the phenomenon.

For example, say that we are examining the motion involved in a random walking movement. We study the motion of an object that is constrained to move along a straight line in the two directions allowed. At each movement, it moves randomly to the right or left, each step being...

Random walks

Random walks are a mathematical model that is used to describe a path that is given by a succession of random steps, which, depending on the system that we want to describe, may have a certain number of degrees of freedom or direction. The term random walk was introduced by Karl Pearson in 1905. In a random walk, each step has a random direction and possibly also a random dimension. It represents a theoretical model to describe any random process through the evolution of known quantities that follow a precise statistical distribution. Physically speaking, the path that we are going to draw over time will not necessarily describe a real motion, but rather indicate more generally the evolution of features over time. This means that random walks find applications in physics, chemistry, and biology, but also in other fields, such as computer science, economics, and sociology...

Markov chains

A Markov chain is a mathematical model of a random phenomenon that evolves over time in such a way that the past influences the future only through the present. The time can be discrete (a whole variable), continuous (a real variable), or, more generally, a totally ordered whole. In this discussion, only discrete chains are considered. Markov chains were introduced in 1906 by Andrei Andreyevich Markov (1856–1922), from whom the name derives.

The example of a one-dimensional random walk seen in the previous section is a Markov chain; the next value in the chain is a unit that is more or less than the current value with the same probability of occurrence, regardless of the way in which the current value was reached.

Stochastic process

...

Summary

In this chapter, we looked at stochastic processes and their applications. We looked at starting a random walk model. Random walks are mathematical models that are used to describe a path given by a succession of random steps, which, depending on the system we want to describe, may have a certain number of degrees of freedom or direction. We have learned how to deal with one-dimensional random walks, and we have seen how to write a code for the simulation of a random walk in the Python language.

Then we were introduced to Markov chains. To understand this topic, you were briefly introduced to probability calculation. The a priori probability, joint probability, and conditional probability were all defined, with examples of their calculation. We then moved on to the definition of Markov chains. A Markov chain is a mathematical model of a random phenomenon that evolves over...

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Key benefits

  • Build projects across robotics, gaming, and finance fields, putting reinforcement learning (RL) into action
  • Get to grips with Keras and practice on real-world unstructured datasets
  • Uncover advanced deep learning algorithms such as Monte Carlo, Markov Decision, and Q-learning

Description

Reinforcement learning has evolved a lot in the last couple of years and proven to be a successful technique in building smart and intelligent AI networks. Keras Reinforcement Learning Projects installs human-level performance into your applications using algorithms and techniques of reinforcement learning, coupled with Keras, a faster experimental library. The book begins with getting you up and running with the concepts of reinforcement learning using Keras. You’ll learn how to simulate a random walk using Markov chains and select the best portfolio using dynamic programming (DP) and Python. You’ll also explore projects such as forecasting stock prices using Monte Carlo methods, delivering vehicle routing application using Temporal Distance (TD) learning algorithms, and balancing a Rotating Mechanical System using Markov decision processes. Once you’ve understood the basics, you’ll move on to Modeling of a Segway, running a robot control system using deep reinforcement learning, and building a handwritten digit recognition model in Python using an image dataset. Finally, you’ll excel in playing the board game Go with the help of Q-Learning and reinforcement learning algorithms. By the end of this book, you’ll not only have developed hands-on training on concepts, algorithms, and techniques of reinforcement learning but also be all set to explore the world of AI.

Who is this book for?

Keras Reinforcement Learning Projects is for you if you are data scientist, machine learning developer, or AI engineer who wants to understand the fundamentals of reinforcement learning by developing practical projects. Sound knowledge of machine learning and basic familiarity with Keras is useful to get the most out of this book

What you will learn

  • Practice the Markov decision process in prediction and betting evaluations
  • Implement Monte Carlo methods to forecast environment behaviors
  • Explore TD learning algorithms to manage warehouse operations
  • Construct a Deep Q-Network using Python and Keras to control robot movements
  • Apply reinforcement concepts to build a handwritten digit recognition model using an image dataset
  • Address a game theory problem using Q-Learning and OpenAI Gym

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Sep 29, 2018
Length: 288 pages
Edition : 1st
Language : English
ISBN-13 : 9781789342093
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Product Details

Publication date : Sep 29, 2018
Length: 288 pages
Edition : 1st
Language : English
ISBN-13 : 9781789342093
Category :
Languages :
Tools :

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Table of Contents

12 Chapters
Overview of Keras Reinforcement Learning Chevron down icon Chevron up icon
Simulating Random Walks Chevron down icon Chevron up icon
Optimal Portfolio Selection Chevron down icon Chevron up icon
Forecasting Stock Market Prices Chevron down icon Chevron up icon
Delivery Vehicle Routing Application Chevron down icon Chevron up icon
Continuous Balancing of a Rotating Mechanical System Chevron down icon Chevron up icon
Dynamic Modeling of a Segway as an Inverted Pendulum System Chevron down icon Chevron up icon
Robot Control System Using Deep Reinforcement Learning Chevron down icon Chevron up icon
Handwritten Digit Recognizer Chevron down icon Chevron up icon
Playing the Board Game Go Chevron down icon Chevron up icon
What's Next? Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.3
(3 Ratings)
5 star 0%
4 star 0%
3 star 66.7%
2 star 0%
1 star 33.3%
P. Casimir-mrowczynski Dec 04, 2018
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
I hate writing less than positive reviews, but this - not cheap - book, initially excited me and then disappointed.The title of the book is a little misleading too. Keras didn't really get used until almost half way through. Before hand it seems to be over complicated explanation and Python code examples.The stock price prediction example was very light weight and perhaps a missed opportunity?Some text seems to be repeated more than necessary also.The code examples ( downloadable ) generally worked first time.The Gym libraries were 'nice' but just goto their website and it's all there.The line-by-line code explanations were ok, but I would have preferred code with comments ( there were none ? ) and a more generic explanation.I didn't really feel I could take the '9 projects' and apply them usefully. Sorry.A brave attempt, but overly wordy, with some ok examples but nothing I feel I can usefully take forward into the real world.I didn't feel 'equipped'.Maybe you will think differently?!
Amazon Verified review Amazon
Edu Apr 04, 2019
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
Lo cierto es que no es un libro tan práctico como prometía ser. Contiene algo de código de keras, y lo explica y comenta todo línea a línea, lo cual está muy bien. Pero los ejemplos son justitos, y tiene la manía de irse por los cerros de Úbeda, explicando por ejemplo lo que es un robot, o explicando teoría de juegos y tipos de juegos de mesa. Esto está muy bien, pero no es lo que yo buscaba con este libro. Es una mezcla entre cultura general, y un poco de práctica.
Amazon Verified review Amazon
Amazon Customer Mar 26, 2019
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Very disappointing book: several problems are not solved using RL or Keras, the introduction to RL and Dynamic Programming is not clear...
Amazon Verified review Amazon
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