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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

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788834247
Length 546 pages
Edition 1st Edition
Languages
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Author (1):
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Maxim Lapan Maxim Lapan
Author Profile Icon Maxim Lapan
Maxim Lapan
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Table of Contents (21) Chapters Close

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 Other Books You May Enjoy 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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