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Deep Reinforcement Learning Hands-On

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

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Table of Contents (21) Chapters

Preface

1. What is Reinforcement Learning?

2. OpenAI Gym FREE CHAPTER

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

Chapter 3. Deep Learning with PyTorch

In the previous chapter, we became familiar with open source libraries, which provided us with a collection of RL environments. However, recent developments in RL, especially its combination with deep learning (DL), now make it possible to solve much more complex and challenging problems than before. This is partly due to the development of DL methods and tools.

This chapter is dedicated to one such tool, which makes it possible to implement complex DL models in just a bunch of lines of Python code. The chapter doesn't pretend to be a complete DL manual, as the field is very wide and dynamic. The goal is to make you familiar with the PyTorch library specifics and implementation details, assuming that you're already familiar with DL fundamentals.

Compatibility note: All of the examples in this chapter were updated for the latest PyTorch 0.4.0, which has a number of changes compared with the previous 0.3.1 release. If you&apos...

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Authors (1)

Maxim Lapan

Maxim Lapan

Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.

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