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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

Chatbots overview

One of the many trending topics of 2017 was AI-driven chatbots. There are various opinions on the subject, ranging from completely useless stuff, to an absolutely brilliant idea, but one thing is hard to question: chatbots open up new ways for people to communicate with computers which are much more human-like and natural than the old-style interfaces that we are all used to.

At its core, a chatbot is a computer program that uses natural language to communicate with other parties (humans or other computer programs) in a form of dialogue. There could be lots of different forms of such a scenario, namely one chatbot talking to a user, or many bots talking to each other, and so on. For example, there might be a technical support bot that can answer free-text questions from users. However, usually chatbots share common properties of a dialogue interaction (the user asks a question, but the chatbot can ask clarifying questions to get the missing information) and a free...

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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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