You're reading from Deep Reinforcement Learning Hands-On Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more

Product type Paperback

Published in Jun 2018

Publisher Packt

ISBN-13 9781788834247

Length 546 pages

Edition 1st Edition

Languages

Python

Tools

Deep Reinforcement Learning

Concepts

Deep Reinforcement Learning

Author (1):

Maxim Lapan

View More author details

Table of Contents (21) Chapters

Preface

1. What is Reinforcement Learning? FREE CHAPTER

2. OpenAI Gym

3. Deep Learning with PyTorch

4. The Cross-Entropy Method

5. Tabular Learning and the Bellman Equation

6. Deep Q-Networks

7. DQN Extensions

8. Stocks Trading Using RL

9. Policy Gradients – An Alternative

10. The Actor-Critic Method

11. Asynchronous Advantage Actor-Critic

12. Chatbots Training with RL

13. Web Navigation

14. Continuous Action Space

15. Trust Regions – TRPO, PPO, and ACKTR

16. Black-Box Optimization in RL

17. Beyond Model-Free – Imagination

18. AlphaGo Zero

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Index

Hardware and software requirements

The examples in this book were implemented and tested using Python version 3.6. I assume that you're already familiar with the language and common concepts such as virtual environments, so I won't cover in detail how to install the package and how to do this in an isolated way. The external libraries we'll use in this book are open source software, including the following:

NumPy: This is a library for scientific computing and implementing matrix operations and common functions.
OpenCV Python bindings: This is a computer vision library, which provides many functions for image processing.
Gym: This is a RL framework developed and maintained by OpenAI with various environments that can be communicated with, in a unified way.
PyTorch: This is a flexible and expressive Deep Learning (DL) library. A short essential crash course on it will be given in the next chapter.
Ptanhttps://github.com/Shmuma/ptan): This is an open source extension to...