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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

Why reinforcement learning?

In 2014, Google acquired a London-based startup named DeepMind for a whopping $500 million. In the news, we read that they had created an AI agent to beat any Atari game, but the main reason why Google paid so much to acquire it was because this breakthrough was a step closer toward general artificial intelligence. General artificial intelligence is referred to as an AI agent. It is capable of doing a variety of tasks and generalizing just like a human. When it surpasses that, that point of singularity is termed, artificial super intelligence. At present, the work done by the AI community is what we term, artificial narrow intelligence, where an AI agent is capable of acing a couple of tasks but not able to generalize over a variety of tasks. 

DeepMind published their paper, Human Level Control through Deep Reinforcement Learning in the...

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