Keras Reinforcement Learning Projects

A practical guide to mastering reinforcement learning algorithms using Keras

Keras Reinforcement Learning Projects

Giuseppe Ciaburro

1 customer reviews
A practical guide to mastering reinforcement learning algorithms using Keras
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Book Details

ISBN 139781789342093
Paperback288 pages

Book 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.

 

Table of Contents

Chapter 2: Simulating Random Walks
Chapter 7: Dynamic Modeling of a Segway as an Inverted Pendulum System
Chapter 8: Robot Control System Using Deep Reinforcement Learning

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

Authors

Table of Contents

Chapter 2: Simulating Random Walks
Chapter 7: Dynamic Modeling of a Segway as an Inverted Pendulum System
Chapter 8: Robot Control System Using Deep Reinforcement Learning

Book Details

ISBN 139781789342093
Paperback288 pages
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