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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

Human demonstrations

The idea behind demonstrations is simple: to help our agent to discover the best way to solve the task, we show it some examples of actions that we think are required for the problem. Those examples could be not the best solution or 100% accurate, but they should be good enough to show the agent promising directions to explore.

In fact, this is a very natural thing to do and all human learning is based on some prior examples given by a teacher in class, your parents or other people. Those examples could be in a written form (recipe books) or given as demonstrations that you need to repeat several times to get it right (dance classes). Such forms of training are much more effective than random search. Just imagine how complicated and lengthy it would be to learn how to clean your teeth by trial-and-error alone. Of course, there is a danger from learning how to follow the demonstrations, which could be wrong or not the most efficient way to solve the problem, but overall...

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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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