README.md · Jhon_tao/Hands-On-Reinforcement-Learning-with-Python

# Hands-On-Reinforcement-Learning-with-Python

This is the code repository for [Hands-On-Reinforcement-Learning-with-Python](https://www.packtpub.com/big-data-and-business-intelligence/hands-reinforcement-learning-python?utm_source=github&utm_medium=repository&utm_campaign=9781788836524), published by Packt.

**Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow**

## What is this book about?
Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms.

This book covers the following exciting features:
* Understand the basics of reinforcement learning methods, algorithms, and elements
* Train an agent to walk using OpenAI Gym and Tensorflow
* Understand the Markov Decision Process, Bellman’s optimality, and TD learning
* Solve multi-armed-bandit problems using various algorithms
* Master deep learning algorithms, such as RNN, LSTM, and CNN with applications

If you feel this book is for you, get your [copy](https://www.amazon.com/dp/1788836529) today!

## Instructions and Navigations
All of the code is organized into folders. For example, Chapter02.

The code will look like the following:
```
policy_iteration():
Initialize random policy
for i in no_of_iterations:
Q_value = value_function(random_policy)
new_policy = Maximum state action pair from Q value
```

**Following is what you need for this book:**
If you’re a machine learning developer or deep learning enthusiast interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you. Some knowledge of linear algebra, calculus, and the Python programming language will help you understand the concepts covered in this book.

With the following software and hardware list you can run all code files present in the book (Chapter 1-15).

### Software and Hardware List

| Chapter  | Software required                   | OS required                        |
| -------- | ------------------------------------| -----------------------------------|
| 1-12     |anaconda                             |Ubutnu or mac                       |
|          | chrome                              | Ubutnu or mac                     |

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. [Click here to download it](http://www.packtpub.com/sites/default/files/downloads/HandsOnReinforcementLearningwithPython_ColorImages.pdf).

### Related products <Paste books from the Other books you may enjoy section>
* Artificial Intelligence with Python [[Packt]](https://www.packtpub.com/big-data-and-business-intelligence/artificial-intelligence-python?utm_source=github&utm_medium=repository&utm_campaign=9781788293778) [[Amazon]](https://www.amazon.com/dp/178646439X)

* Statistics for Machine Learning [[Packt]](https://www.packtpub.com/big-data-and-business-intelligence/statistics-machine-learning?utm_source=github&utm_medium=repository&utm_campaign=9781785280009) [[Amazon]](https://www.amazon.com/dp/1788295757)

## Get to Know the Author
**Sudharsan Ravichandiran**
is a data scientist, researcher, artificial intelligence enthusiast,
and YouTuber (search for Sudharsan reinforcement learning). He completed his bachelors
in information technology at Anna University. His area of research focuses on practical
implementations of deep learning and reinforcement learning, which includes natural
language processing and computer vision. He used to be a freelance web developer and
designer and has designed award-winning websites. He is an open source contributor and
loves answering questions on Stack Overflow.

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