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Reinforcement Learning with TensorFlow

You're reading from  Reinforcement Learning with TensorFlow

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

Table of Contents (21) Chapters close

Title Page
Packt Upsell
Contributors
Preface
1. Deep Learning – Architectures and Frameworks 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 1. Further topics in Reinforcement Learning 2. Other Books You May Enjoy Index

Summary


In this chapter, we learned how reinforcement learning can disrupt the domain of NLP. We studied the reasons behind the use of reinforcement learning in NLP. We covered two big application domains in NLP, that is, text summarization and question answering, and understood the basics of how a reinforcement learning framework was implemented in the existing models to obtain state-of-the-art results. There are other application domains in NLP where reinforcement learning has been implemented, such as dialog generation and machine translation (discussing them is out of the scope of this book).

This brings us to the end of this amazing journey of deep reinforcement learning. We started with the basics by understanding the concepts, then implemented those concepts using TensorFlow and OpenAI Gym, and went through cool research areas where deep reinforcement learning is being implemented at the core level. I hope the journey was interesting and we were able to build the best foundation possible...

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