Practical Deep Reinforcement Learning with Python: Concise Implementation of Algorithms, Simplified Maths, and Effective Use of TensorFlow and PyTorch

Practical Deep Reinforcement Learning with Python: Concise Implementation of Algorithms, Simplified Maths, and Effective Use of TensorFlow and PyTorch (English Edition)
Author: Ivan Gridin
Publisher Finelybook 出版社:‎BPB Publications (July 15, 2022)
Language 语言:‎English
pages 页数:‎398 pages
ISBN-10 书号:‎9355512058
ISBN-13 书号:‎9789355512055

Book Description
Introducing Practical Smart Agents Development using Python, PyTorch, and TensorFlow

Key Features

● Exposure to well-known RL techniques, including Monte-Carlo, Deep Q-Learning, Policy Gradient, and Actor-Critical.

● Hands-on experience with TensorFlow and PyTorch on Reinforcement Learning projects.

● Everything is concise, up-to-date, and visually explained with simplified mathematics.

Description

Reinforcement learning is a fascinating branch of AI that differs from standard machine learning in several ways. Adaptation and learning in an unpredictable environment is the part of this project. There are numerous real-world applications for reinforcement learning these days, including medical, gambling, human imitation activity, and robotics.

This book introduces readers to reinforcement learning from a pragmatic point of view. The book does involve mathematics, but it does not attempt to overburden the reader, who is a beginner in the field of reinforcement learning.

The book brings a lot of innovative methods to the reader's attention in much practical learning, including Monte-Carlo, Deep Q-Learning, Policy Gradient, and Actor-Critical methods. While you understand these techniques in detail, the book also provides a real implementation of these methods and techniques using the power of TensorFlow and PyTorch. The book covers some enticing projects that show the power of reinforcement learning, and not to mention that everything is concise, up-to-date, and visually explained.

After finishing this book, the reader will have a thorough, intuitive understanding of modern reinforcement learning and its applications, which will tremendously aid them in delving into the interesting field of reinforcement learning.

What you will learn

● Familiarize yourself with the fundamentals of Reinforcement Learning and Deep Reinforcement Learning.

● Make use of Python and Gym framework to model an external environment.

● Apply classical Q-learning, Monte Carlo, Policy Gradient, and Thompson sampling techniques.

● Explore TensorFlow and PyTorch to practice the fundamentals of deep reinforcement learning.

● Design a smart agent for a particular problem using a specific technique.

Who this book is for

This book is for machine learning engineers, deep learning fanatics, AI software developers, data scientists, and other data professionals eager to learn and apply Reinforcement Learning to ongoing projects. No specialized knowledge of machine learning is necessary; however, proficiency in Python is desired.

Table of Contents

Part I

1. Introducing Reinforcement Learning

2. Playing Monopoly and Markov Decision Process

3. Training in Gym

4. Struggling With Multi-Armed Bandits

5. Blackjack in Monte Carlo

6. Escaping Maze With Q-Learning

7. Discretization

Part II. Deep Reinforcement Learning

8. TensorFlow, PyTorch, and Your First Neural Network

9. Deep Q-Network and Lunar Lander

10. Defending Atlantis With Double Deep Q-Network

11. From Q-Learning to Policy-Gradient

12. Stock Trading With Actor-Critic

13. What Is Next?


下载地址:

Practical Deep Reinforcement Learning with Python 9789355512055.epub

下载地址 Download隐藏内容需1积分,VIP免费,请先 !没有帐号? 注 册 一个!
觉得文章有用就打赏一下
未经允许不得转载:finelybook » Practical Deep Reinforcement Learning with Python: Concise Implementation of Algorithms, Simplified Maths, and Effective Use of TensorFlow and PyTorch

评论 抢沙发

  • 昵称 (必填)
  • 邮箱 (必填)
  • 网址

觉得文章有用就打赏一下

非常感谢你的打赏,我们将继续给力更多优质内容,让我们一起创建更加美好的网络世界!

支付宝扫一扫打赏

微信扫一扫打赏