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Reinforcement Learning with TensorFlow

You're reading from   Reinforcement Learning with TensorFlow A beginner's guide to designing self-learning systems with TensorFlow and OpenAI Gym

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788835725
Length 334 pages
Edition 1st Edition
Languages
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Author (1):
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Sayon Dutta Sayon Dutta
Author Profile Icon Sayon Dutta
Sayon Dutta
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Toc

Table of Contents (17) Chapters Close

Preface 1. Deep Learning – Architectures and Frameworks FREE CHAPTER 2. Training Reinforcement Learning Agents Using OpenAI Gym 3. Markov Decision Process 4. Policy Gradients 5. Q-Learning and Deep Q-Networks 6. Asynchronous Methods 7. Robo Everything – Real Strategy Gaming 8. AlphaGo – Reinforcement Learning at Its Best 9. Reinforcement Learning in Autonomous Driving 10. Financial Portfolio Management 11. Reinforcement Learning in Robotics 12. Deep Reinforcement Learning in Ad Tech 13. Reinforcement Learning in Image Processing 14. Deep Reinforcement Learning in NLP 15. Further topics in Reinforcement Learning 16. Other Books You May Enjoy

What is Go?

The game of Go originated in China around 3000 years ago. The rules of the game are simple as follows:

  • Go is a two player game
  • The default board size is 19x19 lines
  • One player places a black stone, while the other player places a white stone
  • The goal is to surround the opponent's stones and cover most of the empty spaces on the board

The following is a default board size, which is of 19x19 lines:

19x19 Go board

Even with those simple rules, the game of Go is highly complex. There are around 2.08 x 10170 possible moves in a 19x19 Go compared to 1080 atoms in universe and 10120 possible moves in chess. Thus, the intellectual depth required to play the game of Go has captured human imagination for ages. 

Go versus chess

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