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Python Reinforcement Learning Projects
Python Reinforcement Learning Projects

Python Reinforcement Learning Projects: Eight hands-on projects exploring reinforcement learning algorithms using TensorFlow

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Profile Icon Sean Saito Profile Icon Yang Wenzhuo Profile Icon Shanmugamani
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$19.99 per month
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (1 Ratings)
Paperback Sep 2018 296 pages 1st Edition
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$27.98 $39.99
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$48.99
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Arrow left icon
Profile Icon Sean Saito Profile Icon Yang Wenzhuo Profile Icon Shanmugamani
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$19.99 per month
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (1 Ratings)
Paperback Sep 2018 296 pages 1st Edition
eBook
$27.98 $39.99
Paperback
$48.99
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Free Trial
Renews at $19.99p/m
eBook
$27.98 $39.99
Paperback
$48.99
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Renews at $19.99p/m

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Python Reinforcement Learning Projects

Balancing CartPole

In this chapter, you will learn about the CartPole balancing problem. The CartPole is an inverted pendulum, where the pole is balanced against gravity. Traditionally, this problem is solved by control theory, using analytical equations. However, in this chapter, we will solve the problem with machine learning.

The following topics will be covered in this chapter:

  • Installing OpenAI Gym
  • The different environments of Gym

OpenAI Gym

OpenAI is a non-profit organization dedicated to researching artificial intelligence. Visit https://openai.com for more information about the mission of OpenAI. The technologies developed by OpenAI are free for anyone to use.

Gym

Gym provides a toolkit to benchmark AI-based tasks. The interface is easy to use. The goal is to enable reproducible research. Visit https://gym.openai.com for more information about Gym. An agent can be taught inside of the gym, and learn activities such as playing games or walking. An environment is a library of problems.

The standard set of problems presented in the gym are as follows:

  • CartPole
  • Pendulum
  • Space Invaders
  • Lunar Lander
  • Ant
  • Mountain Car
  • ...

Markov models

The problem is set up as a reinforcement learning problem, with a trial and error method. The environment is described using state_values state_values (?), and the state_values are changed by actions. The actions are determined by an algorithm, based on the current state_value, in order to achieve a particular state_value that is termed a Markov modelIn an ideal case, the past state_values does have an influence on future state_values, but here, we assume that the current state_value has all of the previous state_values encoded. There are two types of state_values; one is observable, and the other is non-observable. The model has to take non-observable state_values into account, as well. That is called a Hidden Markov model.

CartPole...

Summary

In this chapter, you learned about the OpenAI Gym, used in reinforcement learning projects. You saw several examples of the training platform provided out of the box. Then, we formulated the problem of the CartPole, and made the CartPole balance by using a trial and error approach.

In the next chapter, you will learn how to play Atari games by using the Gym and a reinforcement learning approach. 

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Key benefits

  • •Implement Q-learning and Markov models with Python and OpenAI
  • •Explore the power of TensorFlow to build self-learning models
  • •Eight AI projects to gain confidence in building self-trained applications

Description

Reinforcement learning is one of the most exciting and rapidly growing fields in machine learning. This is due to the many novel algorithms developed and incredible results published in recent years. In this book, you will learn about the core concepts of RL including Q-learning, policy gradients, Monte Carlo processes, and several deep reinforcement learning algorithms. As you make your way through the book, you'll work on projects with datasets of various modalities including image, text, and video. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore technologies such as TensorFlow and OpenAI Gym to implement deep learning reinforcement learning algorithms that also predict stock prices, generate natural language, and even build other neural networks. By the end of this book, you will have hands-on experience with eight reinforcement learning projects, each addressing different topics and/or algorithms. We hope these practical exercises will provide you with better intuition and insight about the field of reinforcement learning and how to apply its algorithms to various problems in real life.

Who is this book for?

Python Reinforcement Learning Projects is for data analysts, data scientists, and machine learning professionals, who have working knowledge of machine learning techniques and are looking to build better performing, automated, and optimized deep learning models. Individuals who want to work on self-learning model projects will also find this book useful.

What you will learn

  • •Train and evaluate neural networks built using TensorFlow for RL
  • •Use RL algorithms in Python and TensorFlow to solve CartPole balancing
  • •Create deep reinforcement learning algorithms to play Atari games
  • • Deploy RL algorithms using OpenAI Universe
  • •Develop an agent to chat with humans
  • •Implement basic actor-critic algorithms for continuous control
  • •Apply advanced deep RL algorithms to games such as Minecraft
  • •Autogenerate an image classifier using RL

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Publication date : Sep 29, 2018
Length: 296 pages
Edition : 1st
Language : English
ISBN-13 : 9781788991612
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Length: 296 pages
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Language : English
ISBN-13 : 9781788991612
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Tools :

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Table of Contents

11 Chapters
Up and Running with Reinforcement Learning Chevron down icon Chevron up icon
Balancing CartPole Chevron down icon Chevron up icon
Playing Atari Games Chevron down icon Chevron up icon
Simulating Control Tasks Chevron down icon Chevron up icon
Building Virtual Worlds in Minecraft Chevron down icon Chevron up icon
Learning to Play Go Chevron down icon Chevron up icon
Creating a Chatbot Chevron down icon Chevron up icon
Generating a Deep Learning Image Classifier Chevron down icon Chevron up icon
Predicting Future Stock Prices Chevron down icon Chevron up icon
Looking Ahead Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Christophe Trouillefou Jun 12, 2021
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Un peu ancien (2018), mais explique bien les bases et permet avec ses applications en ligne (via GitHub de l'auteur) de faire pas mal de choses.
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