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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

AlphaGo – Reinforcement Learning at Its Best

Games have the best testing environment for many artificial intelligence (AI) algorithms. These simulated environments are cost effective, and algorithms can be tested in a safe way. The major goal of AI is to solve the biggest problems in the world. The major global objectives for AI are:

  • Eradicate poverty
  • Eradicate hunger
  • Primary personalized healthcare for all
  • Quality education
  • Clean energy
  • Good infrastructure
  • Innovation and creativity
  • Reduced inequalities
  • Protecting the planet
  • Tackle climatic change
  • Peace and justice
  • Good jobs
  • Economic growth
  • Solve water crisis

There are many more global objectives that the research technology and industrial community are trying to achieve. Now with AI algorithms and better computational power, the strides towards these objectives have become longer with time. Though it's a very long...

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