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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 2. Training Reinforcement Learning Agents Using OpenAI Gym FREE CHAPTER 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 touched on the main concepts and challenges related to one of the biggest AI problems, that is, autonomous driving. We learned about the challenges posed by the problem and also learned the current approaches being used to make autonomous driving successful. Moreover, we went through an overview of different sub-tasks of the process, starting from receiving sensory inputs to planning. We also looked at a bit about the famous DeepTraffic simulation where you can test your neural networks to learn efficient movement patterns in heavy traffic. Autonomous driving is itself a vast evolving research topic and covering all of them is beyond the scope of this book. 

In the next chapter, we will study another evolving research hotspot, using AI in finance, where we will learn how reinforcement can help in financial portfolio management.

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