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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

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

Why a continuous space?

All the examples that we've seen so far in the book had a discrete action space, so you might have the wrong impression that discrete actions dominate the field. This is a very biased view, of course, and just reflects the selection of domains that we picked our test problems from. Besides Atari games and simple, classical RL problems, there are lots of tasks requiring more than just making a selection from a small and discrete set of things to do.

To give you an example, just imagine a simple robot with only one controllable joint, which can be rotated in some range of degrees. Usually, to control a physical joint, you have to specify either the desired position or the force applied. In both cases, you need to make a decision about a continuous value. This value is fundamentally different from a discrete action space, as the set of values that you can make a decision on is potentially infinite. For instance, you can ask the joint to move to a...

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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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