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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...

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

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Instant access to your digital eBook copy whilst your Print order is Shipped

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P. Casimir-mrowczynski Dec 04, 2018

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?!

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Edu Apr 04, 2019

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 Customer Mar 26, 2019

Very disappointing book: several problems are not solved using RL or Keras, the introduction to RL and Dynamic Programming is not clear...

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

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Keras Reinforcement Learning Projects

Simulating Random Walks

Random walks

Markov chains

Stochastic process

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

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What you will learn

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