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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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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

Summary

In this chapter, we understood the basic concepts and challenges in the domain of advertising technology. We also learned about the relevant business models, such as CPC, CPM, and CPA, and real-time strategy bidding and why there's a need for an autonomous agent to automate the process. Moreover, we discussed a basic approach to converting the problem state of real-time bidding in online advertising into a reinforcement-learning framework. This is a totally new domain for reinforcement learning to disrupt. Many more exploratory works utilizing reinforcement learning for advertising technology, and their results, are yet to be published.

In the next chapter, we will study how reinforcement learning is being used in the field of computer vision, especially for object detection.

 

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